Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting Non-coding RNA in Vascular Biology and Disease

Only recently have we begun to appreciate the importance and complexity of the non-coding genome, owing in some part to truly significant advances in genomic technology such as RNA sequencing and genome-wide profiling studies. Previously thought to be non-functional transcriptional “noise,” non-coding RNAs (ncRNAs) are now known to play important roles in many diverse biological pathways, not least in vascular disease. While microRNAs (miRNA) are known to regulate protein-coding gene expression principally through mRNA degradation, long non-coding RNAs (lncRNAs) can activate and repress genes by a variety of mechanisms at both transcriptional and translational levels. These versatile molecules, with complex secondary structures, may interact with chromatin, proteins, and other RNA to form complexes with an array of functional consequences. A body of emerging evidence indicates that both classes of ncRNAs regulate multiple physiological and pathological processes in vascular physiology and disease. While dozens of miRNAs are now implicated and described in relative mechanistic depth, relatively fewer lncRNAs are well described. However, notable examples include ANRIL, SMILR, and SENCR in vascular smooth muscle cells; MALAT1 and GATA-6S in endothelial cells; and mitochondrial lncRNA LIPCAR as a powerful biomarker. Due to such ubiquitous involvement in pathology and well-known biogenesis and functional genetics, novel miRNA-based therapies and delivery methods are now in development, including some early stage clinical trials. Although lncRNAs may hold similar potential, much more needs to be understood about their relatively complex molecular behaviours before realistic translation into novel therapies. Here, we review the current understanding of the mechanism and function of ncRNA, focusing on miRNAs and lncRNAs in vascular disease and atherosclerosis. We discuss existing therapies and current delivery methods, emphasising the importance of miRNAs and lncRNAs as effectors and biomarkers in vascular pathology.
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context

In the biomedical domain, the lack of sharable datasets often limit the possibility of developing natural language processing systems, especially dialogue applications and natural language understanding models. To overcome this issue, we explore data generation using templates and terminologies and data augmentation approaches. Namely, we report our experiments using paraphrasing and word representations learned on a large EHR corpus with Fasttext and ELMo, to learn a NLU model without any available dataset. We evaluate on a NLU task of natural language queries in EHRs divided in slot-filling and intent classification sub-tasks. On the slot-filling task, we obtain a F-score of 0.76 with the ELMo representation; and on the classification task, a mean F-score of 0.71. Our results show that this method could be used to develop a baseline system.
Non-Authentication Based Checkpoint Fault-tolerant Vulnerability in Spark Streaming

Apache Spark uses Resilient Distributed Datasets (RDDs) as primitives for data sharing. The in-memory feature of RDD makes Spark faster but it also brings a volatile problem where a failure or a missing RDD causes Spark to recompute all the missing RDD in the lineage. A checkpoint cuts off the lineage by saving the data which is required in the coming computing, thus becoming an essential fault-tolerance mechanism. In this paper, we find that as for Spark Streaming jobs with checkpoint, user authentication is not performed while doing checkpoint during job execution. We present two typical attack scenarios where attackers exploit this vulnerability to interfere with normal users job, causing data loss or even incorrect results. And we put forward a solution which focuses on the administration of checkpoint directory permissions. The experimental results show that our scheme can effectively monitor and resist this attack.
Towards Nearly-linear Time Algorithms for Submodular Maximization with a Matroid Constraint

We consider fast algorithms for monotone submodular maximization subject to a matroid constraint. We assume that the matroid is given as input in an explicit form, and the goal is to obtain the best possible running times for important matroids. We develop a new algorithm for a \emph{general matroid constraint} with a $1 - 1/e - \epsilon$ approximation that achieves a fast running time provided we have a fast data structure for maintaining a maximum weight base in the matroid through a sequence of decrease weight operations. We construct such data structures for graphic matroids and partition matroids, and we obtain the \emph{first algorithms} for these classes of matroids that achieve a nearly-optimal, $1 - 1/e - \epsilon$ approximation, using a nearly-linear number of function evaluations and arithmetic operations.
Proteins of generalist and specialist pathogens differ in their amino acid composition

Primitive amino acids and disorder content in secreted proteins are distinctively enriched in generalist compared with host-specific pathogens. Pathogens differ in their host specificities, with species infecting a unique host (specialist pathogens) and others having a wide host range (generalists). Molecular determinants of pathogen’s host range remain poorly understood. Secreted proteins of generalist pathogens are expected to have a broader range of intermolecular interactions (i.e., higher promiscuity) compared with their specialist counterparts. We hypothesize that this increased promiscuity of generalist secretomes may be based on an elevated content of primitive amino acids and intrinsically disordered regions, as these features are known to increase protein flexibility and interactivity. Here, we measure the proportion of primitive amino acids and percentage of intrinsically disordered residues in secreted, membrane, and cytoplasmic proteins from pathogens with different host specificity. Supporting our prediction, there is a significant general enrichment for primitive amino acids and intrinsically disordered regions in proteins from generalists compared to specialists, particularly among secreted proteins in prokaryotes. Our findings support our hypothesis that secreted proteins' amino acid composition and disordered content influence the pathogens' host range.
Measuring the Spatial Noise of a Low-Cost Eye Tracker to Enhance Fixation Detection

The present study evaluates the quality of gaze data produced by a low-cost eye tracker (The Eye Tribe©, The Eye Tribe, Copenhagen, Denmark) in order to verify its suitability for the performance of scientific research. An integrated methodological framework, based on artificial eye measurements and human eye tracking data, is proposed towards the implementation of the experimental process. The obtained results are used to remove the modeled noise through manual filtering and when detecting samples (fixations). The outcomes aim to serve as a robust reference for the verification of the validity of low-cost solutions, as well as a guide for the selection of appropriate fixation parameters towards the analysis of experimental data based on the used low-cost device. The results show higher deviation values for the real test persons in comparison to the artificial eyes, but these are still acceptable to be used in a scientific setting.
Automatic Detection of Cell Regions in Microscope Images Based on BFED Algorithm

Circulating tumor cells (CTC) attract attention as a biomarker that can evaluate cancer metastasis and therapeutic effects. The CTC exists in the blood of cancer patients, so pathologists analyze blood by using a fluorescence microscope. However, manual analysis by pathologists is hard-work since the number of CTC to substances contained in the blood is very few and the cell regions are often unclear depending on shooting environments. In addition, there are few studies on automatic identification of CTC. In this paper, we develop an automatic detection method of cell regions in microscope images based on bacterial foraging-based edge detection (BFED) algorithm to analyze CTC. In the first step, we detect the initial cell regions by BFED algorithm. Second, we identify whether the region is a single cell or multiple cells come in connect with other cell(s) by SVM. Third, when a cell is connected with other one, we separate the connecting cells by branch and bound algorithm and obtain the final cell regions. We applied our proposed method to 1680 microscopy images (6 cases). The experimental results demonstrate that the proposed method has a true positive rate of 93.9% and a false positive 1.29 /case.
Microscopy Cell Counting and Annotation Using a Max-Tree Representation of the Blood Cell Images

Cell counting is one of the important tasks in cell analysis which is required for different application areas such as medical diagnosis. The method proposed in this paper tries to solve the problem using an image processing-based approach. Max-Tree is a data structure that is widely used in connected morphological operator-based image filtering. Max-Tree provides a hierarchical representation of the image where the root node contains a connected component with the lowest intensity and the leaf nodes hold the connected component with the highest intensity. Cells in an image can be realized as extremal intensity regions where intensity value inside the region is higher than its surroundings, thus can be found in the leaves of the Max-Tree. Counting the number of leaves in the Max-Tree would approximate the number of cells in the image. The Max-Tree also process the pixels of each connected component that can provide us with the annotation of the cells. The proposed method does not require any training or prior annotation by the user. It is conceptually easy and computationally efficient. Performance comparison with the state-of-the-art method ensured its strength.
Probabilistic control of HIV latency and transactivation by the Tat gene circuit

Significance The reservoir of HIV latently infected cells is the major obstacle for complete eradication of HIV infection. Latency and its transactivation in HIV-infected cells are controlled by the intracellular HIV Tat gene circuit. By reconstructing the probability landscape of the circuit through exact solution of the underlying chemical master equation we examined the detailed mechanism of probabilistic intracellular control of latency and transactivation. We show that while the Tat circuit lacks deterministic bistability its probability landscape exhibits bimodality. Moreover, by exploring changes in the probability landscapes under different perturbations, our study suggests effective therapeutic targets for strategies of “shock and kill” to eliminate latently infected cells and “block and lock” to enforce deep latency. The reservoir of HIV latently infected cells is the major obstacle for eradication of HIV infection. The “shock-and-kill” strategy proposed earlier aims to reduce the reservoir by activating cells out of latency. While the intracellular HIV Tat gene circuit is known to play important roles in controlling latency and its transactivation in HIV-infected cells, the detailed control mechanisms are not well understood. Here we study the mechanism of probabilistic control of the latent and the transactivated cell phenotypes of HIV-infected cells. We reconstructed the probability landscape, which is the probability distribution of the Tat gene circuit states, by directly computing the exact solution of the underlying chemical master equation. Results show that the Tat circuit exhibits a clear bimodal probability landscape (i.e., there are two distinct probability peaks, one associated with the latent cell phenotype and the other with the transactivated cell phenotype). We explore potential modifications to reactions in the Tat gene circuit for more effective transactivation of latent cells (i.e., the shock-and-kill strategy). Our results suggest that enhancing Tat acetylation can dramatically increase Tat and viral production, while increasing the Tat–transactivation response binding affinity can transactivate latent cells more rapidly than other manipulations. Our results further explored the “block and lock” strategy toward a functional cure for HIV. Overall, our study demonstrates a general approach toward discovery of effective therapeutic strategies and druggable targets by examining control mechanisms of cell phenotype switching via exactly computed probability landscapes of reaction networks.
Predicting Genes in Single Genomes with AUGUSTUS

AUGUSTUS is a tool for finding protein‐coding genes and their exon‐intron structure in genomic sequences. It does not necessarily require additional experimental input, as it can be applied in so‐called ab initio mode. However, extrinsic evidence from various sources such as transcriptome sequencing or the annotations of closely related genomes can be integrated in order to improve the accuracy and completeness of the annotation. AUGUSTUS can be applied to single genomes, or simultaneously to several aligned genomes. Here, we describe steps required for training AUGUSTUS for the annotation of individual genomes and the steps to do the actual structural annotation. Further, we describe the generation and integration of evidence from various sources of extrinsic evidence. © 2018 by John Wiley & Sons, Inc.
Extensive diversity of malaria parasites circulating in Central African bats and monkeys

Abstract The order Haemosporidia gathers many protozoan parasites which are known to infect many host species and groups. Until recently, the studies on haemosporidian parasites primarily focused on the genus Plasmodium among a wide range of hosts. Genera, like the genus Hepatocystis, have received far less attention. In the present study, we present results of a survey of the diversity of Hepatocystis infecting bats and monkeys living in a same area in Gabon (Central Africa). Phylogenetic analyses revealed a large diversity of Hepatocystis lineages circulating among bats and monkeys, among which certain were previously observed in other African areas. Both groups of hosts harbor parasites belonging to distinct genetic clades and no transfers of parasites were observed between bats and monkeys. Finally, within each host group, no host specificity or geographical clustering was observed for the bat or the primate Hepatocystis lineages.
Estimating the relative importance of demic and cultural diffusion in the spread of the Neolithic in Scandinavia

Using a database of early farming sites in Scandinavia, we estimate that the spread rate of the Neolithic was in the range 0.44–0.66 km yr−1. This is substantially slower (by about 50%) than the rate in continental Europe. We interpret this result in the framework of a new mathematical model that includes horizontal cultural transmission (acculturation), vertical cultural transmission (interbreeding) and demic diffusion (reproduction and dispersal of farmers). To parametrize the model, we estimate reproduction rates of early farmers using archaeological data (sum-calibrated probabilities for the dates of early Neolithic Scandinavian sites) and use them in a wave-of-advance model for the first time. Comparing the model with the archaeological data, we find that the percentage of the spread rate due to cultural diffusion is below 50% (except for very extreme parameter values, and even for them it is below 54%). This strongly suggests that the spread of the Neolithic in Scandinavia was driven mainly by demic diffusion. This conclusion, obtained from archaeological data, agrees qualitatively with the implications of ancient genetic data, but the latter are yet too few in Scandinavia to produce any quantitative percentage for the spread rate due to cultural diffusion. We also find that, on average, fewer than eight hunter–gatherers were incorporated in the Neolithic communities by each group of 10 pioneering farmers, via horizontal and/or vertical cultural transmission.
UvA-DARE ( Digital Academic Repository ) Schema therapy for borderline personality disorder : A qualitative study of patients ’ perceptions

Schema therapy (ST) has been found to be effective in the treatment of borderline personality disorder (BPD). However very little is known about how the therapy is experienced by individuals with BPD including which specific elements of ST are helpful or unhelpful from their perspectives. The aim of this study is to explore BPD patients’ experiences of receiving ST, in intensive group or combined group-individual format. Qualitative data were collected through semi-structured interviews with 36 individuals with a primary diagnosis of BPD (78% females) who received ST for at least 12 months. Participants were recruited as part of an international, multicenter randomized controlled trial (RCT). Interview data (11 Australian, 12 Dutch, 13 German) were analyzed following the procedures of qualitative content analysis. Patients’ perceptions of the benefits gained in ST included improved self-understanding, and better awareness and management of their own emotional processes. While some aspects of ST, such as experiential techniques were perceived as emotionally confronting, patient narratives informed that this was necessary. Some recommendations for improved implementation of ST include the necessary adjunct of individual sessions to group ST and early discussion of therapy termination. Implications of the findings are also discussed, in particular the avenues for assessing the suitability of patients for group ST; management of group conflict and the optimal format for delivering treatment in the intensive group versus combined group-individual formats. PLOS ONE | https://doi.org/10.1371/journal.pone. 0206039 November 21, 2018 1 / 20 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111
Multifunctional Coatings and Nanotopographies: Toward Cell Instructive and Antibacterial Implants

In biomaterials science, it is nowadays well accepted that improving the biointegration of dental and orthopedic implants with surrounding tissues is a major goal. However, implant surfaces that support osteointegration may also favor colonization of bacterial cells. Infection of biomaterials and subsequent biofilm formation can have devastating effects and reduce patient quality of life, representing an emerging concern in healthcare. Conversely, efforts toward inhibiting bacterial colonization may impair biomaterial–tissue integration. Therefore, to improve the long‐term success of medical implants, biomaterial surfaces should ideally discourage the attachment of bacteria without affecting eukaryotic cell functions. However, most current strategies seldom investigate a combined goal. This work reviews recent strategies of surface modification to simultaneously address implant biointegration while mitigating bacterial infections. To this end, two emerging solutions are considered, multifunctional chemical coatings and nanotopographical features.
A Miniaturized Amperometric Hydrogen Sulfide Sensor Applicable for Bad Breath Monitoring

Bad breath or halitosis affects a majority of the population from time to time, causing personal discomfort and social embarrassment. Here, we report on a miniaturized, microelectromechanical systems (MEMS)-based, amperometric hydrogen sulfide (H2S) sensor that potentially allows bad breath quantification through a small handheld device. The sensor is designed to detect H2S gas in the order of parts-per-billion (ppb) and has a measured sensitivity of 0.65 nA/ppb with a response time of 21 s. The sensor was found to be selective to NO and NH3 gases, which are normally present in the oral breath of adults. The ppb-level detection capability of the integrated sensor, combined with its relatively fast response and high sensitivity to H2S, makes the sensor potentially applicable for oral breath monitoring.
Outcome after reconstruction of proximal femoral tumors: A systematic review

Tumor resection followed by reconstruction with a proximal femoral endoprosthesis or an allograft‐prosthesis composite are the two main alternatives for treatment of proximal femoral malignancies. This review describes the revision rate, implant survival, limb salvage rate, and function. Overall revision rates are high and reasons for failure differ between treatment modalities. Rate and reasons for amputation are comparable between both methods. Functional outcome was reasonable to good on average for both treatment modalities. Level of evidence: IV, systematic review and meta‐analysis.
Augmented reality in computer-assisted interventions based on patient-specific 3D printed reference

Augmented reality (AR) can be an interesting technology for clinical scenarios as an alternative to conventional surgical navigation. However, the registration between augmented data and real-world spaces is a limiting factor. In this study, the authors propose a method based on desktop three-dimensional (3D) printing to create patient-specific tools containing a visual pattern that enables automatic registration. This specific tool fits on the patient only in the location it was designed for, avoiding placement errors. This solution has been developed as a software application running on Microsoft HoloLens. The workflow was validated on a 3D printed phantom replicating the anatomy of a patient presenting an extraosseous Ewing's sarcoma, and then tested during the actual surgical intervention. The application allowed physicians to visualise the skin, bone and tumour location overlaid on the phantom and patient. This workflow could be extended to many clinical applications in the surgical field and also for training and simulation, in cases where hard body structures are involved. Although the authors have tested their workflow on AR head mounted display, they believe that a similar approach can be applied to other devices such as tablets or smartphones.
GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint

We know SGAN may have a risk of gradient vanishing. A significant improvement is WGAN, with the help of 1-Lipschitz constraint on discriminator to prevent from gradient vanishing. Is there any GAN having no gradient vanishing and no 1-Lipschitz constraint on discriminator? We do find one, called GAN-QP. To construct a new framework of Generative Adversarial Network (GAN) usually includes three steps: 1. choose a probability divergence; 2. convert it into a dual form; 3. play a min-max game. In this articles, we demonstrate that the first step is not necessary. We can analyse the property of divergence and even construct new divergence in dual space directly. As a reward, we obtain a simpler alternative of WGAN: GAN-QP. We demonstrate that GAN-QP have a better performance than WGAN in theory and practice.
Structural studies of carbons by neutron and x-ray scattering

Carbon can have many different forms and the characterisation of structural features on a length scale of 1 Å to 10 μm is important in defining its physical and chemical properties for the various forms. The use of either electro-magnetic (x-ray) or particle (neutron) beams plays an important role in determining these characteristics. In this paper, we review the various techniques that are used to determine the structural features by experimental means and how the data are processed to give the required information in a suitable form for detailed analysis by computer simulation. Diffraction methods are used for studies of the atomic arrangement and small-angle scattering techniques are used for studies of microporosity in the sample materials. The experimental data obtained from a wide range of different carbon materials are considered and how these results can be used as a basis for modelling the structures in a quantitative manner is also considered. This information underpins their use as active components in a wide range of functional materials.