Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Biomimetic Scaffolds for Tissue Engineering: 3D Printing Techniques in Regenerative Medicine

Three-dimensional (3D) printing technology allows fabricating complex and precise structures by stacking materials layer by layer. The fabrication method has a strong potential in the regenerative medicine field to produce customizable and defect-fillable scaffolds for tissue regeneration. Plus, biocompatible materials, bioactive molecules, and cells can be printed together or separately to enhance scaffolds, which can save patients who suffer from shortage of transplantable organs. There are various 3D printing techniques that depend on the types of materials, or inks, used. Here, different types of organs (bone, cartilage, heart valve, liver, and skin) that are aided by 3D printed scaffolds and printing methods that are applied in the biomedical fields are reviewed.
RKDOSCNV: A Local Kernel Density-Based Approach to the Detection of Copy Number Variations by Using Next-Generation Sequencing Data

Copy number variations (CNVs) are significant causes of many human cancers and genetic diseases. The detection of CNVs has become a common method by which to analyze human diseases using next-generation sequencing (NGS) data. However, effective detection of insignificant CNVs is still a challenging task. In this study, we propose a new detection method, RKDOSCNV, to meet the need. RKDOSCNV uses kernel density estimation method to evaluate the local kernel density distribution of each read depth segment (RDS) based on an expanded nearest neighbor (k-nearest neighbors, reverse nearest neighbors, and shared nearest neighbors of each RDS) data set, and assigns a relative kernel density outlier score (RKDOS) for each RDS. According to the RKDOS profile, RKDOSCNV predicts the candidate CNVs by choosing a reasonable threshold, which it uses split read approach to correct the boundaries of candidate CNVs. The performance of RKDOSCNV is assessed by comparing it with several current popular methods via experiments with simulated and real data at different tumor purity levels. The experimental results verify that the performance of RKDOSCNV is superior to that of several other methods. In summary, RKDOSCNV is a simple and effective method for the detection of CNVs from whole genome sequencing (WGS) data, especially for samples with low tumor purity.
Quantified Morphology of the Cervical and Subdiaphragmatic Vagus Nerves of Human, Pig, and Rat

It is necessary to understand the morphology of the vagus nerve (VN) to design and deliver effective and selective vagus nerve stimulation (VNS) because nerve morphology influences fiber responses to electrical stimulation. Specifically, nerve diameter (and thus, electrode-fiber distance), fascicle diameter, fascicular organization, and perineurium thickness all significantly affect the responses of nerve fibers to electrical signals delivered through a cuff electrode. We quantified the morphology of cervical and subdiaphragmatic VNs in humans, pigs, and rats: effective nerve diameter, number of fascicles, effective fascicle diameters, proportions of endoneurial, perineurial, and epineurial tissues, and perineurium thickness. The human and pig VNs were comparable sizes (∼2 mm cervically; ∼1.6 mm subdiaphragmatically), while the rat nerves were ten times smaller. The pig nerves had ten times more fascicles—and the fascicles were smaller—than in human nerves (47 vs. 7 fascicles cervically; 38 vs. 5 fascicles subdiaphragmatically). Comparing the cervical to the subdiaphragmatic VNs, the nerves and fascicles were larger at the cervical level for all species and there were more fascicles for pigs. Human morphology generally exhibited greater variability across samples than pigs and rats. A prior study of human somatic nerves indicated that the ratio of perineurium thickness to fascicle diameter was approximately constant across fascicle diameters. However, our data found thicker human and pig VN perineurium than those prior data: the VNs had thicker perineurium for larger fascicles and thicker perineurium normalized by fascicle diameter for smaller fascicles. Understanding these differences in VN morphology between preclinical models and the clinical target, as well as the variability across individuals of a species, is essential for designing suitable cuff electrodes and stimulation parameters and for informing translation of preclinical results to clinical application to advance the therapeutic efficacy of VNS.
OPUS – parallel corpora for everyone

Abstract. OPUS is a large collection of freely available parallel corpora that we provide in various formats and packages. All data sets are completely aligned at the sentence level for all possible language pairs. OPUS covers over 200 languages and language variants with a total of about 3.2 billion sentences and sentence fragments containing over 28 billion tokens. The collection contains data from various sources and domains and each sub-corpus is provided in common data formats to make it easy to integrate them in research and development. OPUS also provides tools and on-line interfaces for exploring parts of the collection and is continuously growing in terms of size and coverage.
Kielikone Machine Translation Technology and Its Perspective on the Economics of Machine Translation

This paper describes the machine translation technology of Kielikone Ltd. and gives an outline of TranSmart, a Finnish-English system which is a commercial application of that base technology. We argue that MT is fundamentally empirical research. Product development is a slow and strenuous process, and MT systems will remain incomplete not only vis-à-vis human translations but also with respect to the system’s own potential translation quality. An evaluation method is described which measures the progress in MT development. This method can also be used for system and technology evaluation. The paper concludes with the claim that the real contribution of MT will be seen in the long term in applications that do not compete with human translations but in which MT is the only option
Electrocatalytic Reduction of Nitric Oxide by Cytochrome P450-modified Gold Electrodes

Department of Microbial Engineering and Bio/Molecular Informatics Center, Konkuk University, Seoul 143-701, KoreaReceived October 29, 2003Key Words : Cytochrome P450, NO reduction, Modified electrodes, ImpedanceDetection of NO has drawn much attention as NO playsimportant roles in physiological processes such as neuro-transmission, muscle relaxation and immune system medi-ation.
SLR-SELinux: Enhancing the Security Footstone of SEAndroid with Security Label Randomization

The root privilege escalation attack is extremely destructive to the security of the Android system. SEAndroid implements mandatory access control to the system through the SELinux security policy at the kernel mode, making the general root privilege escalation attacks unenforceable. However, malicious attackers can exploit the Linux kernel vulnerability of privilege escalation to modify the SELinux security labels of the process arbitrarily to obtain the desired permissions and undermine system security. Therefore, investigating the protection method of the security labels in the SELinux kernel is urgent. And the impact on the existing security configuration of the system must also be reduced. This paper proposes an optimization scheme of the SELinux mechanism based on security label randomization to solve the aforementioned problem. At the system runtime, the system randomizes the mapping of the security labels inside and outside the kernel to protect the privileged security labels of the system from illegal obtainment and tampering by attackers. This method is transparent to users; therefore, users do not need to modify the existing system security configuration. A tamper-proof detection method of SELinux security label is also proposed to further improve the security of the method. It detects and corrects the malicious tampering behaviors of the security label in the critical process of the system timely. The above methods are implemented in the Linux system, and the effectiveness of security defense is proven through theoretical analysis and experimental verification. Numerous experiments show that the effect of this method on system performance is less than 1%, and the success probability of root privilege escalation attack is less than 10−9.
Computer vision-based early fire detection using enhanced chromatic segmentation and optical flow analysis technique

Recent advances in video processing technologies have led to a wave of research on computer vision-based fire detection systems. This paper presents a multi-level framework for fire detection that analyses patterns in chromatic information, shape transmutation, and optical flow estimation of fire. First, the decision function of fire pixels based on chromatic information uses majority voting among state-of-the-art fire color detection rules to extract the regions of interest. The extracted pixels are then verified for authenticity by examining the dynamics of shape. Finally, a measure of turbulence is assessed by an enhanced optical flow analysis algorithm to confirm the presence of fire. To evaluate the performance of the proposed model, we utilize videos from the Mivia and Zenodo datasets, which have a diverse set of scenarios including indoor, outdoor, and forest fires, along with videos containing no fire. The proposed model exhibits an average accuracy of 97.2% for our tested dataset. In addition, the experimental results demonstrate that the proposed model significantly reduces the rate of false alarms compared to the other existing models
Terrain Classification Algorithm for Lunar Rover Using a Deep Ensemble Network with High-Resolution Features and Interdependencies between Channels

For terrain classification tasks, previous methods used a single scale or single model to extract the features of the image, used high-to-low resolution networks to extract the features of the image, and used a network with no relationship between channels. These methods would lead to the inadequacy of the extracted features. Therefore, classification accuracy would reduce. The samples in terrain classification tasks are different from in other image classification tasks. The differences between samples in terrain classification tasks are subtler than other image-level classification tasks. And the colours of each sample in the terrain classification are similar. So we need to maintain the high resolution of features and establish the interdependencies between the channels to highlight the image features. This kind of networks can improve classification accuracy. To overcome these challenges, this paper presents a terrain classification algorithm for Lunar Rover by using a deep ensemble network. We optimize the activation function and the structure of the convolutional neural network to make it better to extract fine features of the images and infer the terrain category of the image. In particular, several contributions are made in this paper: establishing interdependencies between channels to highlight features and maintaining a high-resolution representation throughout the process to ensure the extraction of fine features. Multimodel collaborative judgment can help make up for the shortcomings in the design of the single model structure, make the model form a competitive relationship, and improve the accuracy. The overall classification accuracy of this method reaches 91.57% on our dataset, and the accuracy is higher on some terrains.
Logic-based Software Modeling with FOML

Models are at the heart of the emerging Model-Based Systems Engineering ( MBSE ) approach. MBSE is motivated by the growing complexity of software, which requires multiple levels of abstraction that programming languages do not support. In MBSE, models play a central role in the software evolution process. Rich model management must rely on a unifying underlying formal framework that can support, integrate, and mediate powerful modeling services. This paper describes FOML, a F ramework for O bject M odeling with L ogic, its realization in a modeling tool, proves the correctness of class modeling in FOML, illustrates the process of software modeling with the tool, and presents the main features of the system. The FOML framework for software modeling is compact yet powerful, formal, and is based on an underlying logic rule language called PathLP. The combination of class-based conceptualization with a formal logical base enables clean mediation and integration of a wide range of modeling activities and provides a provably correct formulation of class models. Our implementation of FOML features seamless integration of multiple modeling services that simultaneously support multiple models and provide reasoning, meta-reasoning, validation, testing, and evolution services.
Contribution to the formalisation of data-driven invariant modelling constructs of Cyber-Physical Systems. (Contribution à la formalisation d'invariants de modélisation de systèmes cyber-physiques, dirigés par les données)

The digital transformation of collaborative networked manufacturing enterprises requires the building and the applying digital models representing the set of resources and processes knowledge. Modelling such digital copy of the physical system to perform real-time validation and optimization is quite complex and thus needs a big amount of data and some modelling patterns representing the operational semantics of the modelled elements. Generally, the modelling action has a specific application type. For this reason, the core challenge of the digital transformation modelling is to create an invariant approach, namely a decomposable and re-composable model, towards different applications. This PhD thesis aims at identifying and formalising modelling constructs contributing at building informational and functional models for improving the sustainability of manufacturing processes and products based on networked components. The constructs will then allow representing knowledge and its deep relationship with the manufacturing processes. They make the shared knowledge more readily reusable and are at the basis of standardization efforts.
Argininosuccinate lyase from Mycobacterium tuberculosis

Argininosuccinate lyase catalyses the reversible breakdown of argininosuccinate into arginine and fumarate and is known to form tetramers in its quaternary association. The absence of structures involving competent enzymes bound to substrate/products came in the way of the precise elucidation of the catalytic mechanism of this family of proteins. Crystal structures of the enzyme from Mycobacterium tuberculosis in an unliganded form and its complex with the substrate/products have now been determined at 2.2 and 2.7 angstrom, respectively. The refinement of the structure of the complex was bedevilled by the presence of a lattice translocation defect. The two tetramers in the apo-crystals and the one in the crystals of the liganded protein, have the same structure except for the movements associated with enzyme action. Each molecule consists of an N-domain, an M-domain, and a C-domain. The molecule consists of four binding sites, each made up of peptide stretches from three subunits. Three binding sites appear to be occupied by the ligand in the transition state, while the products occupy the fourth site. The structure exhibits the movement of a loop in the M-domain and parts of the C-domain. This is the first instance when the appropriate movements are observed in a complex with bound substrate/product. The detailed picture of the binding site, active site residues and the movements associated with catalysis thus obtained, enabled a revisit of the mechanism of action of the enzyme. (c) 2019 IUBMB Life, 71(5):643-652, 2019
Proteomic Analyses of Morphological Variants of Borrelia burgdorferi Shed New Light on Persistence Mechanisms: Implications for Pathogenesis, Diagnosis and Treatment

Borrelia burgdorferi causes Lyme disease, which is the most common vector borne disease in the United States and Europe. Although 2-4 week antibiotic treatment for Lyme disease is effective in the majority of cases, about 10–20% patients suffer from prolonged post-treatment Lyme disease syndrome (PTLDS). While the mechanisms of PTLDS are unclear, persisting organisms not killed by current Lyme antibiotics has been suggested as a possible explanation. B. burgdorferi can spontaneously develop different morphological variant forms under stress or in stationary phase with increased persistence to antibiotics. To shed light on the possible mechanisms by which these variant forms develop persistence, here, we isolated three B. burgdorferi forms, log phase spirochetal form, stationary phase planktonic form, and stationary phase aggregated biofilm-like microcolony form. We showed that the two separated stationary phase forms especially microcolony form have more persistence to antibiotics than the log phase spirochetal form. Then, we performed mass spectrometry (MS/MS) analysis to determine the proteomic profiles of the three different forms to reveal the mechanisms of persistence in B. burgdorferi. We identified 1023 proteins in the three B. burgdorferi forms, with 642 proteins (63%) differentially expressed. Compared with the log phase spirochetal form of B. burgdorferi, a total of 143 proteins were upregulated in both stationary phase planktonic form and microcolony form. Among these common upregulated proteins, 90 proteins had predicted functions and were mapped to different pathways involved in infection and virulence, DNA repair, heat shock, transport, sporulation, cell envelope and metabolism, many of which are consistent with persister mechanisms in other bacteria. A particularly interesting observation is that infection and virulence related proteins are highly up-regulated in stationary phase planktonic form and microcolony form compared with log phase spirochetal form. These findings shed new light on the mechanisms of B. burgdorferi persistence and offer novel targets for developing more effective diagnostics, vaccines and treatments.
Health Promoting and Disease Preventing Properties of Probiotics with Special Reference to Lactobacillus : A Review

During the last two decades, the knowledge about the proper use of probiotics has been significantly improved. By definition, probiotics are “live microorganisms which when applied in specific amounts by following a proper way can improve the host health”. These beneficial bacteria follow different mechanisms to improve the host health and protect it from the harmful effects of different pathogenic microorganisms. There are different types of microorganisms that are considered as probiotics. This review specially focuses on the probiotic properties of different species of the genus of Lactobacillus. Here we have highlighted different aspects of Lactobacilli for improving animal health such as, their role in immunity, dairy industry, fermentation and their antimicrobial activity. Although, much work has been done in the field of probiotics, still a large part of it needs to be investigated. Currently research has been focused on these probiotic organisms in order to find out their beneficial aspects that are associated with food and health. Scientists are trying to prepare such foods that can provide health benefits along with overcoming nutrient deficiencies. Such kind of food known as functional food can provide better health benefits along with safe nutrient provision. However there is some uncertainty about their role regarding some diseases like their anti-carcinogenic effect, anti-cholesterolaemic effects, anti-oxidative properties, antiulcer properties, anti-inflammatory activity, prevention of genital and urinary tract infections and their potential protective effects against inflammatory bowel diseases. The present review will provide an understanding of the health promoting and disease preventing properties of probiotics in general and Lactobacilli in special.
Rating the disinformation risks of news domains: Global Disinformation Index

Disinformation is the shadowy side to the open internet. It undermines faith in institutions, economies, governments, and even democracy itself. The Global Disinformation Index (GDI) is an assessment of the risk of news domains in media markets around the globe of disin-forming their public. The index is structured as a neutral, independent and transparent assessment of a news sites disinformation risk at the domain level. The following sections set out the methodology and approach of the index, including the use of different disinforma-tion ﬂags to score and determine the disinfor-mation risk rating of a news site.
Geographic and temporal patterns of non-lethal attacks on humpback whales by killer whales in the eastern South Pacific and the Antarctic Peninsula

The role and impact of killer whales Orcinus orca as predators of baleen whales has been emphasized by studies of humpback whales Megaptera novaeangliae. In this study, rake marks on the fluke were used as a proxy for predatory attacks in a sample of 2909 adult humpback whales and 133 calves from 5 breeding and 2 feeding locations in the eastern South Pacific and the Antarctic Peninsula. The goal of this study was to evaluate how often, at what age, where, and when humpback whales were more susceptible to attacks. Overall, 11.5% of adults and 19.5% of calves had rake marks on their flukes. Significant differences were found in the prevalence of scars in calves when comparing breeding (9%) vs. feeding areas (34%) (χ2 = 10.23, p < 0.01). Multi-year sighting analysis of scar acquisition in 120 adults (82% site fidelity) and 37 calves in the Magellan Strait showed no new marks after the initial sighting for the subsequent 15 yr. This finding indicates that rake marks were most probably acquired when whales were calves, which supports the belief that scar acquisition is a once in a lifetime event. The odds of having rake marks increased with time but with a significantly higher rate in calves (χ2 = 5.04, p < 0.05), which suggests an increase in predation pressure over time. Our results support the earlier hypothesis that killer whale attacks occur mostly on calves, near breeding sites, and during the first migration to feeding areas.
Cinética de crecimiento de Gluconacetobacter diazotrophicus usando melaza de caña y sacarosa: evaluación de modelos

Gluconacetobacter diazotrophicus is a plant-growth promoting endophytic bacterium used as a microbial inoculant for different crops. The objective of this work was to apply different mathematical models to represent its growth in a batch submerged culture employing a 3-L bioreactor and using sugarcane molasses and sucrose as energy sources. The time profile of pH, cell biomass, and total sugars was obtained. Models studied were compared considering their fit quality and complexity, and a parametric sensitivity analysis was performed. Four- and five-parameter models with expressions involving substrate and biomass inhibition effects were considered. The Herbert-Pirt-Contois model achieved the highest fit quality with determination coefficients of 0.888 and 0.425 for biomass and substrate, respectively. These values indicate a higher correspondence between the experimental data of biomass concentration and the data calculated by the model, compared to results obtained for total sugars for which this correspondence was lower. This model reached the best combination considering the fit quality and complexity according to the Akaike’s information criterion. The kinetic study performed enabled to observe a bi-phasic behavior in the growth stage of the bacterium when grown on molasses, and a growth limitation effect due to biomass concentration. The outcomes obtained provide a mathematical description useful for design, scale-up, and operation of a future process for the production of a microbial inoculant based on G. diazotrophicus .