Miracle-dz/UniFAR-FTUs huggingface dataset:
The mitomiR/Bcl-2 axis affects mitochondrial function and autophagic vacuole formation in senescent endothelial cells

During senescence, cells undergo distinctive biochemical and morphological changes and become dysfunctional. MiRNAs are involved in the senescence process and specific miRNAs can localize to mitochondria (mitomiRs). We hypothesized that part of the typical alterations of senescence may depends on mitomiRs deregulation. Therefore, we thoroughly explored the phenotype of human endothelial cells undergoing replicative senescence (sHUVECs) and observed elongated/branched mitochondria, accumulation of autophagic vacuoles (AVs), increased ROS and IL-1β production and reduced expression of Bcl-2 compared to younger cells (yHUVECs). Despite these pro-apoptotic features, sHUVECs are more resistant to serum deprivation, conceivably due to development of pro-survival strategies such as upregulation of Bcl-xL and Survivin. We demonstrate that mitomiR-181a, -34a, and -146a, are overexpressed and localize to mitochondria in sHUVECs compared with yHUVECs and that they: i) down-regulate Bcl-2, ii) induce permeability transition pore opening and activation of caspase-1 and 3, iii) affect sensitivity to apoptosis and iv) promote the conversion of LC3-I to LC3-II. Overall, we document for the first time that some mitomiRs can act as mediators of the multiple but functionally linked biochemical and morphological changes that characterize aging cells and that they can promote different cellular outcomes according to the senescence status of the cell.
PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution

We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. To strengthen the training-test overlap, we collect a large corpus of 38K documents and 12.5M words which are mostly from the vocabulary of English-speaking preschoolers. Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance. The dataset is freely available at https://preschool-lab.github. io/PreCo/.
Learning to Geolocalise Tweets at a Fine-Grained Level

Fine-grained geolocation of tweets has become an important feature for reliably performing a wide range of tasks such as real-time event detection, topic detection or disaster and emergency analysis. Recent work adopted a ranking approach to return a predicted location based on content-based similarity to already available individual geotagged tweets. However, this work made use of the IDF weighting model to compute the ranking, which can diminish the quality of the Top-N retrieved tweets. In this work, we adopt a learning to rank approach towards improving the effectiveness of the ranking and increasing the accuracy of fine-grained geolocalisation. To this end we propose a set of features extracted from pairs of geotagged tweets generated within the same fine-grained geographical area (squared areas of size 1 km). Using geotagged tweets from two cities (Chicago and New York, USA), our experimental results show that our learning to rank approach significantly outperforms previous work based on IDF ranking, and improves accuracy of tweet geolocalisation at a fine-grained level.
Privacy Protection for Flexible Parametric Survival Models

Data privacy is a major concern in modern society. In this work, we propose two solutions to the privacy-preserving problem of regression models on medical data. We focus on flexible parametric models which are powerful alternatives to the well-known Cox regression model. For the first approach, we propose a sampling mechanism which guarantees differential privacy for flexible parametric survival models. We first transform the likelihood function of the models to guarantee that likelihood values are bounded. We then use a Hamiltonian Monte-Carlo sampler to sample a random parameter vector from the posterior distribution. As a result, this random vector satisfies the requirement for differential privacy. For the second approach, as predictions with high accuracy and high confidence are very important for medical applications, we propose a mechanism which protects privacy by randomly perturbing the posterior distribution. We can then use the sampler to draw multiple random samples of the perturbed posterior to estimate the credible intervals of the parameters. The proposed mechanism does not guarantee differential privacy for the perturbed posterior. However, it allows controlling the contribution of each individual data record to the posterior. In the worst case scenario, when all data records are revealed except the target data record, the random noise added to the posterior would make it extremely difficult to obtain the target data record. The experiments conducted on two real datasets show that our proposed approaches outperform state-of-the-art methods in predicting the survival rate of individuals.
Tactile Symbol Discrimination on a Small Pin-array Display

Pin-array displays are a promising technology that allow to display visual information with touch, a crucial issue for blind and partially sighted users. Such displays are programmable, therefore can considerably increase, vary and tailor the amount of information as compared to common embossed paper and, beyond Braille, they allow to display graphics. Due to a shortage in establishing which ideal resolution allows to understand simple graphical concepts, we evaluated the discriminability of tactile symbols at different resolutions and complexity levels in blind, blindfolded low-vision and sighted participants. We report no differences in discrimination accuracy between tactile symbols organized in 3x3 as compared to 4x4 arrays. A metric based on search and discrimination speed in blind and in low-vision participants does not change at different resolutions, whereas in sighted participants it significantly increases when resolution increases. We suggest possible guidelines in designing dictionaries of low-resolution tactile symbols. Our results can help designers, ergonomists and rehabilitators to develop usable human-machine interfaces with tactual symbol coding.
Beacon in the Dark: A System for Interactive Exploration of Large Email Corpora

The large amount of heterogeneous data in these email corpora renders experts' investigations by hand infeasible. Auditors or journalists, e.g., who are looking for irregular or inappropriate content or suspicious patterns, are in desperate need for computer-aided exploration tools to support their investigations. We present our Beacon system for the exploration of such corpora at different levels of detail. A distributed processing pipeline combines text mining methods and social network analysis to augment the already semi-structured nature of emails. The user interface ties into the resulting cleaned and enriched dataset. For the interface design we identify three objectives expert users have: gain an initial overview of the data to identify leads to investigate, understand the context of the information at hand, and have meaningful filters to iteratively focus onto a subset of emails. To this end we make use of interactive visualisations based on rearranged and aggregated extracted information to reveal salient patterns.
Improve Network Embeddings with Regularization

Learning network representations is essential for many downstream tasks such as node classification, link prediction, and recommendation. Many algorithms derived from SGNS (skip-gram with negative sampling) have been proposed, such as LINE, DeepWalk, and node2vec. In this paper, we show that these algorithms suffer from norm convergence problem, and propose to use L2 regularization to rectify the problem. The proposed method improves the embeddings consistently. This is verified on seven different datasets with various sizes and structures. The best improvement is 46.41% for the task of node classification.
Improving Logistics Processes in Inland Container Terminal

This paper focuses on the application of two approaches: Business Process Redesign (BPR) and Discrete Event Simulation (DES) in an attempt to enhance the basic logistics processes in inland container terminals. The goal of the paper is to identify the weaknesses and potential problems in the execution of current logistics processes, as well as to redesign such identified weaknesses and problems. The main contribution of the paper is to show how DES and BPR can be used in restructuring of container logistics processes in inland container terminals. Its original feature is a novelty, a holistic approach in rationalization of container logistics processes in inland intermodal terminals, creating models of the current (AS-IS) and improved (TO-BE) states. Models show that DES and BPR represent an easily accessible and effective tool in the analysis of terminal processes in container terminals. Therefore, it can be efficiently used for monitoring the current situation, identification of weak spots in a particular system and for creating any necessary preconditions for improvement of business processes.
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions

Detecting controversy in general web pages is a daunting task, but increasingly essential to efficiently moderate discussions and effectively filter problematic content. Unfortunately, controversies occur across many topics and domains, with great changes over time. This paper investigates neural classifiers as a more robust methodology for controversy detection in general web pages. Current models have often cast controversy detection on general web pages as Wikipedia linking, or exact lexical matching tasks. The diverse and changing nature of controversies suggest that semantic approaches are better able to detect controversy. We train neural networks that can capture semantic information from texts using weak signal data. By leveraging the semantic properties of word embeddings we robustly improve on existing controversy detection methods. To evaluate model stability over time and to unseen topics, we asses model performance under varying training conditions to test cross-temporal, cross-topic, cross-domain performance and annotator congruence. In doing so, we demonstrate that weak-signal based neural approaches are closer to human estimates of controversy and are more robust to the inherent variability of controversies.
Evaluation of apoptosis markers in different cell lines infected with equine arteritis virus

ABSTRACT Equine arteritis virus (EAV) induces apoptosis in infected cells. Cell death caused by EAV has been studied mainly using three cell lines, BHK-21, RK-13 and Vero cells. The mechanism of apoptosis varies among cell lines and results cannot be correlated owing to differences in EAV strains used. We evaluated different markers for apoptosis in BHK-21, RK-13 and Vero cell lines using the Bucyrus EAV reference strain. Acridine orange/ethidium bromide staining revealed morphological changes in infected cells, while flow cytometry indicated the extent of apoptosis. We also observed DNA fragmentation, but the DNA ladder was detected at different times post-infection depending on the cell line, i.e., 48, 72 and 96 h post-infection in RK-13, Vero and BHK-21 cells, respectively. Measurement of viral titers obtained with each cell line indicated that apoptosis causes interference with viral replication and therefore decreased viral titers. As an unequivocal marker of apoptosis, we measured the expression of caspase-3 and caspases-8 and -9 as extrinsic and intrinsic markers of apoptosis pathways, respectively. Caspase-8 in BHK-21 cells was the only protease that was not detected at any of the times assayed. We found that Bucyrus EAV strain exhibited a distinctive apoptosis pathway depending on the cell line.
Unfractionated and Low Molecular Weight Heparin Reduce Platelet Induced Epithelial-Mesenchymal Transition in Pancreatic and Prostate Cancer Cells

The interaction with platelets is of crucial importance for tumor cells passing through hematogenous metastasis. Platelets protect cancer cells from immune surveillance and exhibit many other prometastatic effects. Notably, platelets can change the epithelial tumor phenotype, a process termed epithelial-mesenchymal transition (EMT), which confers stem cell-like properties onto tumor cells associated with an increased motility and drug resistance. The aim of the study is to investigate the impact of heparin on the platelet induced EMT program in pancreatic and prostate tumor cells. Platelet activation and interaction with cancer cells were determined by static adhesion assays. Applying ELISAs, the platelet release of EMT inducing mediators was quantified. EMT marker protein expression by tumor cells was explored by western blot and qPCR. Our data show that different tumor cell entities have different platelet binding capacities and also that a weak interaction is sufficient to change tumor cell phenotype. Additionally, unfractionated heparin (UFH) as well as low molecular weight heparin (LMWH) reduced tumor cell platelet interaction. Subsequently, attenuated platelet-derived mediator release resulted in reduced EMT marker protein and transcription factor expression by the cancer cells and decreased cell migration. These data suggest that heparin reduces platelet induced EMT program and prevents the formation of cancer cells with stem cell-like properties. This additional mechanism argues for the use of heparin in oncological applications.
Frequency of post-stroke pneumonia: Systematic review and meta-analysis of observational studies

Background Post-stroke pneumonia and other infectious complications are serious conditions whose frequency varies widely across studies. Aims We conducted a systematic review to estimate the frequency of post-stroke pneumonia and other types of major infection. Summary of review MEDLINE, EMBASE, CINAHL, and PsycINFO databases were searched for prospective studies with consecutive recruitment of stroke patients. The primary outcome was post-stroke pneumonia. Secondary outcomes were any infection and urinary tract infection. Quality assessment was done using Newcastle Ottawa scale. Heterogeneity of estimates across study populations was calculated using Cochran's Q (heterogeneity χ2) and I2 statistics. A total of 47 studies (139,432 patients) with 48 sample populations were eligible for inclusion. Mean age of patients was 68.3 years and their mean National Institute of Health Stroke Scale score was 8.2. The pooled frequency of post-stroke pneumonia was 12.3% (95% confidence interval [CI] 11%–13.6%; I2 = 98%). The pooled frequency from 2011 to 2017 was 13.5% (95% CI 11.8%–15.3%; I2 = 98%) and comparable with earlier periods (P interaction = 0.31). The pooled frequency in studies in stroke units was 8% (95% CI 7.1%–9%; I2 = 78%) and significantly lower than other locations (P interaction = 0.001). The pooled frequency of post-stroke infection was 21% (95% CI 13%–29.3%; I2 = 99%) and of post-stroke urinary tract infection was 7.9% (95% CI 6.7%–9.3%; I2 = 96%). Conclusion Approximately 1 in 10 stroke patients experience pneumonia during the acute period of hospital care. The frequency of post-stroke pneumonia has remained stable in recent decades but is lower in patients receiving stroke unit care compared to management in other ward settings.
Equine Methicillin-Resistant Sequence Type 398 Staphylococcus aureus (MRSA) Harbor Mobile Genetic Elements Promoting Host Adaptation

Continuing introduction of multi-drug resistant, zoonotic pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) in horse clinics challenges the biosafety of employees and animal patients. This study was aimed to determine the occurrence of mobile genetic elements facilitating survival in the early stages of invasive infection in different host species, including humans and horses, in MRSA carried by equine patients admitted to a large horse clinic. A total of 341 equine patients were investigated for carriage of MRSA by hygiene screening directly at hospital admission. MRSA were further investigated by antimicrobial susceptibility testing, whole-genome sequencing and genomic composition, including virulence factors involved in immune evasion and host adaption. From a total of 340 validated specimens from equine nostrils, 3.5% yielded positive results for MRSA. All MRSA were found to be closely related belonging to sequence type (ST) 398_t011 with up to four additional antimicrobial resistances. All MRSA harbored a specific Staphylococcal Pathogenicity Island (SaPIbov5) involved in facilitating survival in ruminant and equine plasma. Moreover, a β-hemolysin (hlb) converting ΦSa3 phage encoding the human-specific Immune Evasion Cluster (IEC) was present in 72% of the isolates. An equid-specific leukotoxin encoded by a further temperate phage (Saeq1) was only rarely detected (22%). Despite the absence of β-hemolysin production for all IEC-positive ST398, a prominent hemolysis zone was demonstrable on sheep blood agar. Thus, IEC might remain undetected among the ST398 lineage, since the presence of IEC is commonly associated with reduction of hemolysis in S. aureus belonging to other genetic backgrounds. Here we describe MRSA-ST398 harboring different mobile genetic elements encoding variants of immune evasion factors and toxins previously shown to contribute to S. aureus invasive diseases in specific host species or ecologic niches. We suggest these combinations contribute to the adaptation of MRSA belonging to ST398 with respect to epidemic spread across different habitats and hosts, and may therefore confer a host “generalist” phenotype.
Predicting the response to neoadjuvant therapy for early-stage breast cancer: tumor-, blood-, and imaging-related biomarkers

Neoadjuvant therapy (NAT) has been used increasingly in patients with locally advanced or early-stage breast cancer. However, the accurate evaluation and prediction of response to NAT remain the great challenge. Biomarkers could prove useful to identify responders or nonresponders, or even to distinguish between early and delayed responses. These biomarkers could include markers from the tumor itself, such as versatile proteins, genes, and ribonucleic acids, various biological factors or peripheral blood cells, and clinical and pathological features. Possible predictive markers could also include multiple features from functional imaging, such as standard uptake values in positron emission tomography, apparent diffusion coefficient in magnetic resonance, or radiomics imaging biomarkers. In addition, cells that indirectly present the immune status of tumor cells and/or their host could also potentially be used as biomarkers, eg, tumor-infiltrating lymphocytes, tumor-associated macrophages, and myeloid-derived suppressor cells. Though numerous biomarkers have been widely investigated, only estrogen and/or progesterone receptors and human epidermal growth factor receptor have been proven to be reliable biomarkers to predict the response to NAT. They are the only biomarkers recommended in several international guidelines. The other aforementioned biomarkers warrant further validation studies. Some multigene profiling assays that are commercially available, eg, Oncotype DX and MammaPrint, should be used with caution when extrapolated to NAT settings. A panel of combined multilevel biomarkers might be able to predict the response to NAT more robustly than individual biomarkers. To establish such a panel and its prediction model, reliable methods and extensive clinical validation are warranted.
mHealth for diabetes self-management in the Kingdom of Saudi Arabia: barriers and solutions

Purpose In this paper I present a perspective of mobile health (mHealth) technologies for diabetes in conjunction with an overview of the current status of mHealth technologies for diabetes self-management and the clinical evidence in the Kingdom of Saudi Arabia. In addition, a small survey to identify the barriers to mHealth for diabetes care in the Kingdom and the relevant solutions are discussed. Participants and methods In order to study the relevant obstacles for adopting mHealth solutions for diabetes care and to suggest appropriate solutions, a small survey study was conducted with a specific questionnaire deployed to >40 anonymous leading health care professionals and decision-makers of the Kingdom. The survey was distributed by means of a link to the target population through a WhatsApp group. The data were collected during 1 month, and three reminders were sent to the group to complete the survey. Basic descriptive statistics were used to analyze the survey data. Results The high percentages of agreement among the participants suggest that mHealth expertise and human shortage; funding and infrastructure investments; legal, privacy standardization and regulatory obstacles; and health care organizational and bureaucracy impediments are barriers to the implementation of mHealth technologies in the self-management of diabetes in this region. However, most of these barriers can be mitigated by the creation of the appropriate leadership and clinical environment in the current health care system. Conclusion The mHealth technologies for diabetes self-management have not yet been translated successfully in the Kingdom of Saudi Arabia as it was detected in the literature review carried out in this work. Among other possibilities, the causes of this situation are the existence of human, organizational, funding, bureaucracy, and legal barriers, as shown by the results of the survey conducted among decision-makers and senior clinicians of the Kingdom of Saudi Arabia.
GYANI: An Indexing Infrastructure for Knowledge-Centric Tasks

In this work, we describe GYANI (gyan stands for knowledge in Hindi), an indexing infrastructure for search and analysis of large semantically annotated document collections. To facilitate the search for sentences or text regions for many knowledge-centric tasks such as information extraction, question answering, and relationship extraction, it is required that one can query large annotated document collections interactively. However, currently such an indexing infrastructure that scales to millions of documents and provides fast query execution times does not exist. To alleviate this problem, we describe how we can effectively index layers of annotations (e.g., part-of-speech, named entities, temporal expressions, and numerical values) that can be attached to sequences of words. Furthermore, we describe a query language that provides the ability to express regular expressions between word sequences and semantic annotations to ease search for sentences and text regions for enabling knowledge acquisition at scale. We build our infrastructure on a state-of-the-art distributed extensible record store. We extensively evaluate GYANI over two large news archives and the entire Wikipedia amounting to more than fifteen million documents. We observe that using GYANI we can achieve significant speed ups of more than 95x in information extraction, 53x on extracting answer candidates for questions, and 12x on relationship extraction task.