Miracle-dz/UniFAR-FTUs huggingface dataset:
The pro-inflammatory response to influenza A virus infection is fueled by endothelial cells

A co-culture model of human primary endothelial cells and organoid-derived epithelial cells shows that endothelial cells are abortively infected by influenza A virus but can drive the pro-inflammatory response. Morbidity and mortality from influenza are associated with high levels of systemic inflammation. Endothelial cells play a key role in systemic inflammatory responses during severe influenza A virus (IAV) infections, despite being rarely infected in humans. How endothelial cells contribute to systemic inflammatory responses is unclear. Here, we developed a transwell system in which airway organoid–derived differentiated human lung epithelial cells were co-cultured with primary human lung microvascular endothelial cells (LMECs). We compared the susceptibility of LMECs to pandemic H1N1 virus and recent seasonal H1N1 and H3N2 viruses and assessed the associated pro-inflammatory responses. Despite the detection of IAV nucleoprotein in LMEC mono-cultures, there was no evidence for productive infection. In epithelial–endothelial co-cultures, abundant IAV infection of epithelial cells resulted in the breakdown of the epithelial barrier, but infection of LMECs was rarely detected. We observed a significantly higher secretion of pro-inflammatory cytokines in LMECs when co-cultured with IAV-infected epithelial cells than LMEC mono-cultures exposed to IAV. Taken together, our data show that LMECs are abortively infected by IAV but can fuel the inflammatory response.
Group A Streptococcus antibiotic tolerance in necrotizing fasciitis

Objectives Group A Streptococcus (GAS) necrotizing fasciitis (NF) is a difficult-to-treat bacterial infection associated with high morbidity and mortality despite extensive surgery and targeted antibiotic treatment. Bacteria surviving prolonged antibiotic exposure without displaying genetic resistance, so-called persisters, are associated with difficult-to-treat infections, such as GAS-NF. In the present study, we investigated the presence of persistent GAS in tissue freshly debrided from three NF patients and examined more in depth the persisters phenomenon in GAS-NF clinical isolates. Methods Time-lapse imaging of freshly isolated GAS-NF clinical isolates, the image analysis software ColTapp and antibiotic challenge-based persisters assays were used to assess the presence of persisters. Results We show for the first time that GAS recovered directly from freshly debrided NF patient’s tissue are characterized by increased colony appearance time heterogeneity, indicating the presence of persisters. Acidic pH or nutrient stress exposure, mimicking the NF-like environment in vitro, similarly leads to phenotypic heterogeneity resulting in enhanced antibiotic survival and confirming the presence of GAS persisters. Conclusions GAS persisters are present in the tissue freshly debrided from GAS-NF patients and might be one explanation for antibiotic treatment failure and surgery requirement in GAS-NF. Tailored treatment options, including the use of persisters-targeting drugs, need to be developed to increase GAS-NF therapy success.
Evaluation of tumor antigen-specific antibody responses in patients with metastatic triple negative breast cancer treated with cyclophosphamide and pembrolizumab

The role of B cells in antitumor immunity is becoming increasingly appreciated, as B cell populations have been associated with response to immune checkpoint blockade (ICB) in patients with breast cancer and murine models of breast cancer. Deeper understanding of antibody responses to tumor antigens is needed to clarify the function of B cells in determining response to immunotherapy. We evaluated tumor antigen-specific antibody responses in patients with metastatic triple negative breast cancer treated with pembrolizumab following low-dose cyclophosphamide therapy using computational linear epitope prediction and custom peptide microarrays. We found that a minority of predicted linear epitopes were associated with antibody signal, and signal was associated with both neoepitopes and self-peptides. No association was observed between signal presence and subcellular localization or RNA expression of parent proteins. Patient-specific patterns of antibody signal boostability were observed that were independent of clinical response. Intriguingly, measures of cumulative antibody signal intensity relative to immunotherapy treatment showed that the one complete responder in the trial had the greatest increase in total antibody signal, which supports a potential association between ICB-dependent antibody boosting and clinical response. The antibody boost in the complete responder was largely driven by increased levels of IgG specific to a sequence of N-terminal residues in native Epidermal Growth Factor Receptor Pathway Substrate 8 (EPS8) protein, a known oncogene in several cancer types including breast cancer. Structural protein prediction showed that the targeted epitope of EPS8 was in a region of the protein with mixed linear/helical structure, and that this region was solvent-exposed and not predicted to bind to interacting macromolecules. This study highlights the potential importance of the humoral immune response targeting neoepitopes as well as self epitopes in shaping clinical response to immunotherapy.
The impact of α-synuclein aggregates on blood-brain barrier integrity in the presence of neurovascular unit cells

The role of the blood-brain barrier (BBB) is to control trafficking of biomolecules and protect the brain. This function can be compromised by pathological conditions. Parkinson’s disease (PD) is characterized by the accumulation of α-synuclein aggregates (αSN-AGs) such as oligomers and fibrils, which contribute to disease progression and severity. Here we study how αSN-AGs affect the BBB in in vitro co-culturing models consisting of human brain endothelial hCMEC/D3 cells alone and co-cultured with astrocytes and neurons/glial cells. When cultivated on their own, hCMEC/D3 cells were compromised by αSN-AGs, which decreased cellular viability, mitochondrial membrane potential, wound healing activity, TEER and permeability parameters, as well as increased the levels of ROS and NO. Co-culturing of these cells with activated microglia also increased BBB impairment according to TEER and systemic immune cell transmigration assays. In contrast, hCMEC/D3 cells co-cultured with astrocytes or dopaminergic neurons or simultaneously treated with their conditioned media showed increased resistance against αSN-AGs. Our work demonstrates the complex relationship between members of the neurovascular unit (NVU) (perivascular astrocytes, neurons, microglia, and endothelial cells), αSN-AGs and BBB. Graphical Abstract
High Performance Mixed Graph-Based Concurrency Control

Modern applications are often built on top of many-core OLTP databases. In such systems, concurrency control is an essential component in achieving high performance. Graph-based concurrency control was historically deemed nonviable due to concerns over the computational costs of maintaining an acyclic conflict graph. This conventional wisdom has been refuted by recent research. The work conducted in this PhD has sought to further investigate the usefulness of graph-based concurrency control. Specifically, we propose mixed serialization graph testing (MSGT), a concurrency control protocol that allows transactions to concurrently execute at different isolation levels whilst minimizing unnecessary aborts. MSGT combines a recently proposed concurrent graph data structure with Adya’s mixing-correct theorem . The practical utility of MSGT is illustrated by a survey of isolation levels supported by 24 ACID databases. MSGT has been implemented in a prototype many-core database and a preliminary evaluation using an augmented YCSB workload performed. Initial results indicate the efficacy of MSGT at leveraging transactions specified with weaker isolation requirements. In such workloads, MSGT is able to outperform serializable graph-based concurrency control by up to 23%. We cast further doubt on the notion that graph-based concurrency control in many-core OLTP databases is impractical.
Context based Data Quality Rules for Multidimensional Data

Data quality evaluation and improvement is an important asset in every system, particularly in systems which aim is to analyse data, such as those that are based on multidimensional data models. When talking about data quality the main approach found in literature is fitness for use which means that data quality cannot be evaluated nor improved without taking context into account. Evaluating data quality over systems with a multidimensional model is clearly context dependent. However, there is not enough generality in the solutions found in the literature for context-based data quality management, which means that for every particular case the problem needs to be redefined. In this PhD proposal we aim to reach to a formal definition of every concept mentioned above and their interactions. As a result it would be possible that, given a particular multidimensional model and its context, a set of Data Quality rules can be generated in a simple way.
Towards Legally and Ethically Correct Online HTN Planning for Data Transfer

Data transfer among servers is crucial for distributed data mining because many databases are distributed around the world. However, as data privacy is becoming more legally and ethically protected, it is necessary to abide by the laws and respect the ethical guidelines when transferring and utilizing data. Because information affecting legal/ethical decision making is often distributed, the data-transfer plan must be updated online when new information is obtained while transferring data among servers. In this study, we propose a dynamic hierarchical task network (HTN) planning method that considers legal and ethical norms while planning multihop data transfers and data analyses/transformations. In our knowledge representation, we show that data-transfer tasks can be represented by the task-decomposition rules of total-order HTN planning. We also show that legal norms can be expressed as the preconditions of tasks and actions, and ethical norms can be expressed as the costs of tasks and actions where legal norms cannot be violated, but ethical norms can be violated if necessary following the ethical theory of utilitarianism. In the middle of the plan execution, the online planner dynamically updates the plan based on new information obtained in accordance with laws and ethical guidelines.
MAT-Builder: a System to Build Semantically Enriched Trajectories

The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, traffic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect tra-jectories, the community suffers from a general lack of publicly available datasets. This is due to privacy concerns that make it difficult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to different types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-BUILDER, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-BUILDER has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities to the system and set up their own semantic enrichment process. The demonstration scenario, which will be showcased during the demo session, highlights how MAT-BUILDER's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.
LTL Model Checking using Coinductive Answer Set Programming

We present a model checker for Linear Temporal Logic using Goal-Directed Answer Set Programming under Costable model semantics (CoASP). Costable model semantics allows for positive loops to succeed unlike Stable model semantics where positive loops fail. Therefore, by using the Costable model semantics, LTL formulas involving the 𝒢 and ℛ operator can be proved coinductively.
Improving Conversational Evaluation via a Dependency-Aware Permutation Strategy

The rapid growth in number and complexity of conversational agents has highlighted the need for suitable evaluation tools to describe their performance. Current offline conversational evaluation approaches rely on collections composed of multiturn conversations, each including a sequence of utterances. Such sequences represent a snapshot of reality: a single dialog between the user and a hypothetical system on a specific topic. We argue that this paradigm is not realistic enough: multiple users will ask diverse questions in variable order, even for a conversation on the same topic. In this work 1 we propose a dependency-aware utterances sampling strategy to augment data available in conversational collections while maintaining temporal dependencies within conversations. Using the sampled conversations, we show that the current evaluation framework favours specific systems while penalizing others, leading to biased evaluation. We further show how to exploit dependency-aware utterances permutations in our current evaluation framework and increase the power of statistical evaluation tools such as ANOVA.
A Survey of Sentimental Analysis Methods on COVID-19 Research

In this era of social media, people share anything they feel or experience on social media in the form of posts or comments. These posts, comments or reviews of the people can be analyzed using sentimental analysis, which is emerging field in text mining. COVID-19 has people's life all over the globe and thus has declared as pandemic. Due to COVID, people are feeling panic, anxiety, rage, sorrow, misery, stress and other issues. In this review, we have presented the sentimental analysis data sources, approaches, scenarios, methods and tools by comparing thirty studies. The results illustrated that most researchers have used SVM and Naive Bayes for sentimental analysis on COVID research. We also concluded that most of the researchers work on the sentiments of students, reopening sentiments, vaccine sentiments etc. © 2022 CEUR-WS. All rights reserved.
OpenTRIAGE: Entity Linkage for Detail Webpages

We present OpenTriage, a system for extracting structured entities from detail Web pages of several sites and finding linkages between the extracted data. The system builds an integrated knowledge base by leveraging the redundancy of information with an Open Information Extraction approach: it incrementally processes all the available pages while discovering new attributes. It is based on a hybrid human-machine learning technique that targets a desired quality level. After two preliminary tasks, i.e., blocking and extraction, OpenTriage interleaves two integration tasks, i.e., linkage, and matching, while managing the uncertainty by means of very simple questions that are posed to an external oracle.
LPG-based Ontologies as Schemas for Graph DBs

Graph DBs are an emerging NoSQL technology that is boosting the opportunity of data handling based on interconnection and processing of single instances, rather than batch processing as usual in traditional relational DBs. Differently from relational DBs, the most prominent graph DB, Neo4j, is schema-less and based on the LPG graph model. We propose the definition and uses of ontologies as schemas, which would also enable high-level (logical) automated reasoning on the data. The graph model adopted by standard approaches to ontologies in Computer Science is incompatible with the LPG model. So, we propose a technology, called GraphBRAIN, specifically designed to exploit the full representational power of LPGs, still having a mapping to standard ontological approaches. GraphBRAIN also allows to apply different schemas on one underlying graph, representing different but inter-related views on the same data, and to combine schemas. This paper describes the formalism and outlines its possible applications. Development and implementation of the technology is ongoing, and a prototype is available and running.
Accounting for Bossy Users in Context-Aware Group Recommendations

Lots of activities, like watching a movie or going to the restaurant, are intrinsically group-based. To recommend such activities to groups, traditional single-user recommendation techniques are not appropriate and, as a consequence, over the years a number of group recommender systems have been developed. Recommending items to be enjoyed together by a group of people poses many ethical challenges: in fact, a system whose unique objective is to achieve the best recommendation accuracy might learn to disadvantage submissive users in favor of more aggressive ones. In this work we investigate the ethical challenges of context-aware group recommendations, in the general case of ephemeral groups (i.e., groups where the members might be together for the first time), using a method that can recommend also items that are new to the system. We show the goodness of our method on two real-world datasets. The first one is a very large dataset containing the personal and group choices regarding TV programs of 7,921 users w.r. t. sixteen contexts of viewing, while the second one gathers the musical preferences (both individual and in groups) of 280 real users w.r. t. two contexts of listening. Our extensive experiments show that our method always manages to obtain the highest recall while delivering ethical guarantees in line with the other fair group recommender systems tested.
Conditional Syntax Splitting, Lexicographic Entailment and the Drowning Effect

Lexicographic inference [1] is a well-behaved and popular approach to reasoning with non-monotonic conditionals. In recent work we have shown that lexicographic inference satisfies syntax splitting, which means we can restrict our attention to parts of the belief base that share atoms with a given query. In this paper, we introduce the concept of conditional syntax splitting, inspired by the notion of conditional independence as known from probability theory. We show that lexicographic inference satisfies conditional syntax splitting, and connect conditional syntax splitting to several known properties from the literature on non-monotonic reasoning, including the drowning effect.
Reasoning in Warded Datalog+/- with Harmful Joins

Warded Datalog+/- is a powerful member of the Datalog+/- family of logic languages, featuring full support for recursion and existential quantification. As a result of the promising trade-off between expressive power and data complexity offered, it recently rose as a relevant candidate for ontological reasoning on large knowledge graphs and is employed as logic core of the Vadalog system, a well-known state-of-the-art reasoner. To achieve decidability and data tractability in practice over recursive settings, reasoners such as Vadalog adopt specialized strategies that control the effects of recursion and ensure reasoning termination with small memory footprint. However, exploiting the theoretical underpinnings of the Warded fragment, to enable these strategies, requires the settings not to contain “harmful” joins, i.e., on variables affected by existential quantification. To support reasoning decidability and the full expressive power of the language, we developed the Harmful Join Elimination, an algorithm that removes such joins while preserving the correctness of the task, and we integrated it into the Vadalog system.
Correlation Clustering: from Local to Global Constraints

Given a set of data objects, consider that object pairs are assigned two weights expressing the advantage of putting those objects in the same cluster or in separate clusters, respectively. Correlation clustering partitions the input object set so as to minimize the sum of the intra-cluster negative-type weights plus the sum of the inter-cluster positive-type weights. Existing approximation algorithms provide quality guarantees if the weights are bounded in some way. Regardless of the type, the weight bounds that have been so far studied are local bounds , i.e., constraints that are required to hold for every object pair in isolation. In this paper, we discuss global weight bounds in correlation clustering, and in particular, we derive bounds on edge weights’ aggregate functions that are sufficient to lead to proved quality guarantees. Our formulation extends the range of applicability of the most prominent existing correlation-clustering algorithms thus providing benefits, both theoretical and practical. Also, we showcase our results in a real-world scenario of feature selection for fair clustering.
Constrained Default Logic Programming

This paper develops a new formalism CDLP by combining ASP and constrained default logic to facilitate modeling questions with incomplete information, such that both Reiter’s defaults and constraint defaults can be represented directly. After that, a method to split the CDLP programs is proposed by extending the notion of splitting sets to accommodate its property of constraint nonmonotonicity. Finally, we design a primary algorithm for solving the CDLP programs.
Scalable Discovery of Queries over Event Streams

Complex event processing (CEP) detects situations of interest by matching patterns in a continuous stream of events. Queries for these patterns can be learned automatically in the presence of event data that is labelled with the situation to detect. However, existing event query discovery methods deal with scaling issues in a somewhat ad-hoc manner. In this paper, we therefore outline our plan to characterize the design space for event query discovery, instantiate it with concrete algorithms, and provide means to recommend algorithms based on properties of the event data used as input. Specifically, we consider four dimensions in the design of discovery algorithms that search the space of candidate queries. We also identify the core functionality underlying the respective algorithms and provide initial results on their realization.
Describing Multidimensional Data Through Highlights

The Intentional Analytics Model (IAM) is a new paradigm to couple OLAP and analytics. It relies on two ideas: (i) letting the user explore data by expressing his/her analysis intentions rather than the data (s)he needs, and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of model components (e