Miracle-dz/UniFAR-FTUs huggingface dataset:
Comb-structured mRNA vaccine tethered with short double-stranded RNA adjuvants maximizes cellular immunity for cancer treatment

Integrating antigen-encoding mRNA and immunostimulatory adjuvant into a single formulation is a promising approach to potentiating the efficacy of mRNA vaccines. Here, we developed a scheme based on RNA engineering to integrate adjuvancy directly into antigen-encoding mRNA strands without hampering the ability to express antigen proteins. Short double-stranded RNA (dsRNA) was designed to target retinoic acid-inducible gene-I (RIG-I), an innate immune receptor, for effective cancer vaccination and then tethered onto mRNA strand via hybridization. Tuning the dsRNA structure and microenvironment by changing its length and sequence enabled the determination of the structure of dsRNA-tethered mRNA efficiently stimulating RIG-I. Eventually, the formulation loaded with dsRNA-tethered mRNA of the optimal structure effectively activated mouse and human dendritic cells and drove them to secrete a broad spectrum of proinflammatory cytokines without increasing the secretion of anti-inflammatory cytokines. Notably, the immunostimulating intensity was tunable by modulating the number of dsRNA along mRNA strand, which prevents excessive immunostimulation. Versatility in the applicable formulation is a practical advantage of the dsRNA-tethered mRNA. Its formulation with three existing systems, i.e., anionic lipoplex, ionizable lipid-based lipid nanoparticles, and polyplex micelles, induced appreciable cellular immunity in the mice model. Of particular interest, dsRNA-tethered mRNA encoding ovalbumin (OVA) formulated in anionic lipoplex used in clinical trials exerted a significant therapeutic effect in the mouse lymphoma (E.G7-OVA) model. In conclusion, the system developed here provides a simple and robust platform to supply the desired intensity of immunostimulation in various formulations of mRNA cancer vaccines.
Implications of taxonomic bias for microbial differential-abundance analysis

Differential-abundance (DA) analyses enable microbiome researchers to assess how microbial species vary in relative or absolute abundance with specific host or environmental conditions, such as health status or pH. These analyses typically use sequencing-based community measurements that are taxonomically biased to measure some species more efficiently than others. Understanding the effects that taxonomic bias has on the results of a DA analysis is essential for achieving reliable and translatable findings; yet currently, these effects are unknown. Here, we characterized these effects for DA analyses of both relative and absolute abundances, using a combination of mathematical theory and data analysis of real and simulated case studies. We found that, for analyses based on species proportions, taxonomic bias can cause significant errors in DA results if the average measurement efficiency of the community is associated with the condition of interest. These errors can be avoided by using more robust DA methods (based on species ratios) or quantified and corrected using appropriate controls. Wide adoption of our recommendations can improve the reproducibility, interpretability, and translatability of microbiome DA studies. This manuscript was rendered from commit 3bde34f of https://github.com/mikemc/differential-abundance-theory. Supporting data analyses can be found in the accompanying computational research notebook. Please post comments or questions on GitHub. The manuscript is licensed under a CC BY 4.0 License. See the GitHub Releases or Zenodo record for earlier versions.
Genome-wide DNA methylation profiles in smoking discordant and concordant monozygotic twin pairs

Smoking-associated DNA methylation levels identified through epigenome-wide association studies (EWAS) are generally ascribed to smoking-reactive mechanisms, but the contribution of a shared genetic predisposition to smoking and DNA methylation levels is typically not accounted for. We exploited a strong within-family design, i.e., the discordant monozygotic twin design, to study reactiveness of DNA methylation in blood cells to smoking and reversibility of methylation patterns upon quitting smoking. Illumina HumanMethylation450 BeadChip data were available for 769 monozygotic twin pairs (mean age=36 years,range=18-78, 70% female), including pairs discordant or concordant for current or former smoking. In pairs discordant for current smoking, 13 differentially methylated CpGs were found between current smoking twins and their genetically identical co-twin who never smoked. Top sites include multiple CpGs in CACNA1D and GNG12, which encode subunits of a calcium voltage-gated channel and G protein, respectively. These proteins interact with the nicotinic acetylcholine receptor, suggesting that methylation levels at these CpGs might be reactive to nicotine exposure. All 13 CpGs have been previously associated with smoking in unrelated individuals and data from monozygotic pairs discordant for former smoking indicated that methylation patterns are to a large extent reversible upon smoking cessation. We further showed that differences in smoking level exposure for monozygotic twins who are both current smokers but differ in the number of cigarettes they smoke are reflected in their DNA methylation profiles. In conclusion, by analysing data from monozygotic twins, we robustly demonstrate that DNA methylation level in human blood cells is reactive to cigarette smoking.
On the $\ell$-adic Hodge group of nonsimple Abelian varieties

We study the product decomposition of algebraic monodromy groups attached to nonsimple Abelian varieties, giving essentially the best possible statement that only involves the (relative) dimensions of the simple factors. We also explicitly construct families of examples showing these results cannot be improved, and give an application to Abelian varieties of dimension at most 5.
A Study of Spirituality in Relationship with Perceived Happiness Status, Perceived Health Status and Academic Achievement among Adolescents

Adolescence is a sensitive period of life which is accompanied by a series of biological, psychological, and cognitive changes in an individual. This age of confusion and immaturity predisposes individuals towards risky behaviour like alcohol/drug abuse, violence, sexual experimentation etc. The Mental Health Foundation (2009) enlisted various protective factors for adolescents that can help in keeping them safe from risky behaviour. These were: spirituality, social support, parental support, meaningful relationships, constructive hobbies etc. Spirituality is a relationship of an individual with the Divine, with oneself and with others. Houston and Cartwright (2007) defined spirituality using four components. The first was a belief in transcendence, something higher than oneself. The second was interconnectedness, which meant the sense of unification with all living beings. The third component was a feeling of love and compassion for others. Fourth component included a sense of purpose and meaning in life. Spirituality has been consistently linked with happiness, better health outcomes and general feelings of wellbeing among adolescents. The present study aimed to investigate the relationship of spirituality with perceived happiness status, perceived health status, and academic achievement among adolescents. A sample of 100 adolescents (50 males and 50 females) in the age range of 16 and 18 years was included in the study. Inter correlation Analysis was conducted to find the relationship between the variables. In order to find out differences between two groups viz. males and females, t tests were employed. Results showed a significant relationship between spirituality, perceived happiness status and academic achievement of adolescents. This study highlighted the importance of spirituality, and how it can be meaningful not only in the emotional and psychological domain, but the cognitive domain as well. research study to determine, the on academic success of successful African-American Quantitative data was collected using two survey instruments, the Religious Commitment Inventory and a demographic instrument for data collection. Qualitative data was collected five researcher-conducted focus group with ten African- a of the States. Results showed that there was a disparity between the perceived and the actual relationship between academic success and religious commitment. Adolescents perceived that religious played a significant and positive role in their academic success. Quantitative
Automatic Time-Series Clustering via Network Inference

Given a collection of multidimensional time-series that contains an unknown type and number of network structures between variables, how efficiently can we find typical patterns and their points of variation? How can we interpret important relationshipswithobtainedpatterns? Inthispaper,weproposeanewmethodofmodel-basedclustering,whichwecallnetwork clustering via graphical lasso (NGL). Our method has the following properties: (a) Interpretable : it provides interpretable network structures and cluster assignments for the data; (b) Automatic : it determines the optimal cut points and the number of clusters automatically; (c) Accurate : it provides reliable clustering performance thanks to the automated algorithm. We evaluate our NGL algorithm on both real and synthetic datasets, obtaining interpretable network structure results and outperforming state-of-the-art baselines in terms of accuracy.
SoCRATe: a Framework for Compensating Users Over Time with Limited Availability Recommendations

We present our preliminary ideas for developing SoCRATe, a framework and an online system dedicated to providing recommendations to users when items’ availability is limited. SoCRATe is relevant to several real-world applications, among which movie and task recommendations. SoCRATe has several appealing features: it watches users as they consume recommendations and accounts for user feedback in refining recommendations in the next round, it implements loss compensation strategies to make up for sub-optimal recommendations, in terms of accuracy, when items have limited availability, and it decides when to re-generate recommendations on a need-based fashion. SoCRATe accommodates real users as well as simulated users to enable testing multiple recommendation choice models. To frame evaluation, SoCRATe introduces a new set of measures that capture recommendation accuracy over time as well as throughput and user satisfaction. All these features make SoCRATe unique and able to adapt recommendations to user preferences in a resource-limited setting.
An Overview of Vadalog: a System for Reasoning over Large Knowledge Graphs

As a main requirement, a knowledge representation and reasoning language must exhibit a good tradeoff between expressive power and computational complexity of reasoning. Warded Datalog+/-, a fragment from the Datalog+/- family, satisfies these requirements, by offering high expressive power and at the same time preserving tractability of query answering. The Vadalog system, a knowledge graph management system developed by the University of Oxford and Banca d’Italia, adopts Warded Datalog+/-as its core language. This enables Vadalog to solve many ontological reasoning tasks including those where complex graph traversal operations are involved. To control graph navigation, Vadalog combines different rules prioritization policies and join implementations. In this paper, a short version of our recent contribution appeared in the Journal of Information Systems, we describe Vadalog from an architectural point of view, focusing on the execution model, graph traversal strategies, and join algorithms. We also provide an experimental evaluation of the system.
Augmenting Digital Textbooks with Reusable Smart Learning Content: Solutions and Challenges

A powerful set of educational tools has emerged over the last decade with the rise in the adoption of online adaptive learning content. An increasingly popular tool in this space is the “intelligent textbook” as a platform to support and distribute content for e-learning, given its re-semblance with real-life physical books. Existing efforts in this direction include the development of digital textbooks where both textual content and interactive learning activities (i.e., examples, problems, etc.) are carefully handcrafted by the authors so that they are perfectly placed to follow the knowledge acquisition-practice flow. However, this approach is very time-consuming, and it requires the work of high-expertise authors. In this work, we suggest and discuss a scalable solution: we take existing digital textbooks and augment them by using repositories of existing on-line learning material associated with the subject matter. We present our current work in this direction and discuss challenges and opportunities for the future work.
Semantic Shift Detection in Vatican Publications: a Case Study from Leo XIII to Francis

In the recent years, word embedding models are being proposed to effectively detect language change and semantic shift in diachronic corpora. In this paper, we present a comparative analysis of different word embedding approaches by considering a case-study based on an Italian diachronic corpus of Vatican publications of Popes from Leo XIII to Francis (1898-2020). Four different approaches are considered, characterized by the adoption of different embedding models each one trained over the publications of a specific pope. The paper aims to explore whether and how word embedding techniques are successful in detecting semantic shifts over the language used by popes. Consequently, been the embedding comparison across different
There and Back Again: Combining Non-monotonic Logical Reasoning and Deep Learning on an Assistive Robot

This paper describes the development of an architecture that combines non-monotonic logical reasoning and deep learning in virtual (simulated) and real (physical) environments for an assistive robot. As an illustrative example, we consider a robot assisting in a simulated restaurant environment. For any given goal, the architecture uses Answer Set Prolog to represent and reason with incomplete commonsense domain knowledge, providing a sequence of actions for the robot to execute. At the same time, reasoning directs the robot’s learning of deep neural network models for human face and hand gestures made in the real world. These learned models are used to recognize and translate human gestures to scenarios that mimic real-world situations in the simulated environment, and to goals that need to be achieved by the robot in the simulated environment. We report the challenges faced in the development of such an integrated architecture, as well as the insights learned from the design, implementation, and evaluation of this architecture by a distributed team of researchers during the ongoing pandemic.
Generating Synthetic Discrete Datasets with Machine Learning

The real data are not always available/accessible/sufficient or in many cases they are incomplete and lacking in semantic content necessary to the definition of optimization processes. In this paper we discuss about the synthetic data generation under two different perspectives. The core common idea is to analyze a limited set of real data to learn the main patterns that characterize them and exploit this knowledge to generate brand new data. The first perspective is constraint-based generation and consists in generating a synthetic dataset satisfying given support constraints on the real frequent patterns. The second one is based on probabilistic generative modeling and considers the synthetic generation as a sampling process from a parametric distribution learned on the real data, typically encoded as a neural network (e.g. Variational Autoencoders, Generative Adversarial Networks).
Recent Advancements on Bio-Inspired Hebbian Learning for Deep Neural Networks

Deep learning is becoming more and more popular to extract information from multimedia data for indexing and query processing. In recent contributions, we have explored a biologically inspired strategy for Deep Neural Network (DNN) training, based on the Hebbian principle in neuroscience. We studied hybrid approaches in which unsupervised Hebbian learning was used for a pre-training stage, followed by supervised fine-tuning based on Stochastic Gradient Descent (SGD). The resulting semi-supervised strategy exhibited encouraging results on computer vision datasets, motivating further interest towards applications in the domain of large scale multimedia content based retrieval. methodology, and we show experimental results in computer vision. The results are promising, motivating further interest in the application of our approach to the domain of multimedia content retrieval on a large scale.
Predicting Vehicles Parking Behaviour for EV Recharge Optimization

The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems
Explaining Link Prediction with Kelpie

Link Prediction (LP) is the problem of inferring new facts in a Knowledge Graph from the already known ones. Recent advances in Machine Learning have led researchers to develop LP models that represent Knowledge Graph elements as vectors in an embedding space. This approach has led to greatly encouraging results, often outperforming traditional approaches; its main shortcoming is its opaqueness, hindering both our understanding and our trust in these models. In this context, we discuss the Kelpie explainability framework. Kelpie can be applied to any embedding-based LP model and can identify the combinations of training facts that have enabled the prediction of a given link. Kelpie can extract two complementary types of explanations, that we dub necessary and sufficient .
Laboratory evaluation of soil geotechnical properties via electrical conductivity

In recent years, the use of electrical methods has been improved considerably in several areas such as environment, agronomy, geophysics and geotechnics. Electrical resistivity may be used as a parameter in soil characterization; it may have many advantages for the determination of geotechnical factors influencing the behavior of soil. In this research, laboratory tests were carried out on natural sodic soil from the region of Ain Nouissy (Mostaganem) to investigate the geotechnical and chemical behavior of a natural soil using electrical resistivity measurements under various conditions. This study aims therefore to identify relationships between electrical resistivity of the soil and some physical and chemical parameters such as water content, porosity, salt concentration and pH. Moreover, electrical resistivity has been measured with the addition of salts into the natural soil. The results show that electrical resistivity reduced drastically with the addition of salt. The obtained results will be used to evaluate properties needed for soil characterization, as well as to track soil behavior with respect to salt concentration for use in soil remediation.
Summary on "Hybrid Neuro-Symbolic Approach for Text-Based Games using Inductive Logic"

In this paper, I briefly describe the summary of my work titled - Hybrid Neuro-Symbolic Approach for Text-Based Games using Inductive Logic Programming . Text-based games (TBGs) have emerged as an important test-bed, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning. A key challenge for agents solving this task is to generalize across multiple games and shows good results on both seen and unseen objects. To tackle these issues, we have designed a hybrid neuro-symbolic framework for TBGs that uses symbolic reasoning along with the neural RL model. We also use WordNet as an external commonsense knowledge source to bring information to generalize the hypothesis. We have tested our work on different settings on TWC games and showed that the agents that incorporate the neuro-symbolic hybrid approach with the generalized rules outperform the baseline agents.
Bias Score: Estimating Gender Bias in Sentence Representations

The ever-increasing number of applications based on semantic text analysis is making natural language understanding a fundamental task. Language models are used for a variety of tasks, such as parsing CVs or improving web search results. At the same time, concern is growing around embedding-based language models, which often exhibit social bias and lack of transparency, despite their popularity and widespread use. Word embeddings in particular exhibit a large amount of gender bias, and they have been shown to reflect social stereotypes. Recently, sentence embeddings have been introduced as a novel and powerful technique to represent entire sentences as vectors. However, traditional methods for estimating gender bias cannot be applied to sentence representations, because gender-neutral entities cannot be easily identified and listed. We propose a new metric to estimate gender bias in sentence embeddings, named bias score. Our solution, leveraging the semantic importance of individual words and previous research on gender bias in word embeddings, is able to discern between correct and biased gender information at sentence level. Experiments on a real-world dataset demonstrates that our novel metric identifies gender stereotyped sentences.
Assumable Answer Set Programming

For modeling the assumption-based intelligent agents who make assumptions and use them to construct their belief sets, this paper proposes a logic programming language AASP (Assumable Answer Set Programming) by extending ASP (Answer Set Programming). Rational principles of assumption-based reasoning are given to define the semantics of the AASP program. The relation of AASP to some existing default formalism and extensions of ASP implies that AASP can be used to model and solve some interesting problems with incomplete information in the framework of assumption-based reasoning.
Gender Discriminatory Language Identification with an Hybrid Algorithm based on Syntactic Rules and Machine Learning

In the last years, gender discrimination in textual documents has emerged as an open problem and is undergoing analysis. The difficulty of identifying sentences in which this discrimination is present is linked to the context used and the formalisms adopted. This work describes an exploratory activity linked to the context of regulations and official documents of Italian public administrations. A hybrid algorithm based on syntactic rules and machine learning is therefore proposed, capable of identifying a specific subset of possible gender discrimination