Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a rapid and highly accurate method for 13C tracer-based metabolomics and its application on a hydrogenotrophic methanogen

Microfluidic capillary electrophoresis-mass spectrometry (CE-MS) is a rapid and highly accurate method to determine isotopomer patterns in isotopically labeled compounds. Here, we developed a novel method for tracer-based metabolomics using CE-MS for underivatized proteinogenic amino acids. The method consisting of a ZipChip CE system and a high-resolution Orbitrap Fusion Tribrid mass spectrometer allows us to obtain highly accurate data from 1 μL of 100 nmol/L mol amino acids comparable to a mere 1 × 104-5 prokaryotic cells. To validate the capability of the CE-MS method, we analyzed 16 protein-derived amino acids from a methanogenic archaeon Methanothermobacter thermautotrophicus as a model organism, and the mass spectra showed sharp peaks with low mass errors and background noise. Tracer-based metabolome analysis was then performed to identify the central carbon metabolism in M. thermautotrophicus using 13C-labeled substrates. The mass isotopomer distributions of serine, aspartate, and glutamate revealed the co-occurrence of the Wood-Ljungdahl pathway and an incomplete reductive TCA cycle for carbon fixation. In addition, biosynthesis pathways of 15 amino acids were constructed based on the mass isotopomer distributions of the detected protein-derived amino acid, genomic information, and public database. Among them, the presence of the alternative enzymes of alanine dehydrogenase, ornithine cyclodeaminase, and homoserine kinase was suggested in the biosynthesis pathways of alanine, proline, and threonine, respectively. To our knowledge, the novel 13C tracer-based metabolomics using CE-MS is the most efficient method to identify central carbon metabolism and amino acid biosynthesis pathways and is applicable to in any kind of isolated microbe.
Mating system and environment predict the direction and extent of introgression between incipient Clarkia species

Introgression is pervasive across the tree of life, varying across taxa, geography, and genomes. However, we are only beginning to understand the factors that modulate this variation and how they may be affected by global change. Here, we used 200 genomes and a 15-year site-specific environmental dataset to investigate the effect of mating system divergence and environmental variation on the magnitude of introgression between two recently diverged annual plants. Two subspecies of Clarkia xantiana diverged ca. 65k years ago and subsequently came into secondary sympatry where they form replicated contact zones. We found that introgression is asymmetric between taxa, with substantially more introgression from the self-fertilizing taxon to the outcrossing taxon. This asymmetry is caused by a bias in the direction of initial F1 hybrid formation and subsequent backcrossing. We also found extensive variation in the outcrosser’s admixture proportion among contact zones, which is predicted nearly entirely by interannual variance in spring precipitation. Greater fluctuations in spring precipitation result in higher admixture proportions, likely mediated by the effects of spring precipitation on the expression of traits that determine premating reproductive isolation. Climate-driven hybridization dynamics may be particularly affected by global change, potentially reshaping species boundaries and adaptation to novel environments.
Vascular Plants Collected on the Veslovskiy Peninsula, Kunashir Island in 2012

During a field trip in 2012 to the Veslovskiy peninsula of Kunashir Island in the southern Kurils, 72 species in 24 families of vascular plants were collected. The floristic composition of Veslovskiy was compared with Notsuke of eastern Hokkaido. The general flora is similar between the two regions, except that forest vegetation is lacking in Velslovkiy. The relative dominance of the families; Rosaceae, Ranunculaceae, Ericaceae, and Violaceae in Notsuke can be explained by the presence of the forest vegetation. We could not confirm the forest vegetation in Veslovskiy. Rhinanthus minor and Leontodon autumnalis, which occur on Kunashir, are naturalized plants peculiar to Far Eastern Russia. They may become invaders on Hokkaido. Cakile edentula, on sandy beaches, has recently invaded the southern Kurils via seed dispersal through ocean currents from northern Hokkaido or Sakhalin.
Role of IL-6 and IL10 immune response genes against nematode Haemonchus contortus in ovis aries- a novel report

Haemonchus contortus is one of the most deadly parasites affecting sheep industry causing heavy economic loss. In our earlier study, we have reported RIGI, an earlier established antiviral molecule and CD14 (an earlier established antibacterial molecule) to have potential role in antiparasitic immunity for the first time. In this current study, we aim to clone and sequence the IL-6 and IL10 genes in sheep (Ovis aries) and utilized bioinformatics methods to analyze and compare the functional and structural domains of peptides produced from IL6 and IL10. Furthermore, the study explored these domains in three-dimensional structures. The findings of this study provide molecular characterization of IL-6 and IL10 in sheep, which is essential for advancing our understanding of functional immune responses in this animal. We report IL6 and IL10 to have potential antiparasitic role against H. contortus for first time. The production of these molecules opens up possibilities for its potential use as recombinant proteins to manipulate the immune response. Evaluating its value in vaccine research and gaining a broader understanding of its role in immune responses becomes feasible with the availability of IL-6 and IL10. Moreover, the differential mRNA expression levels of IL-6, IL10 between healthy and diseased sheep indicate its crucial role in maintaining homeostasis and mounting immune responses. IL10 acts as receptor for H. contortus. The identification of this immune-responsive gene also offers new avenues for investigating long-term resistance to H. contortus infection in sheep.
Pest risk analysis in Europe - how can risks of invasive alien species be assessed and managed?

Pest risk analysis (PRA) is a tool used in plant health to assess risks of quarantine pests or other organisms harmful to plants and to identify options for their management. Standards of the International Plant Protection Convention (IPPC) and the European and Mediterranean Plant Protection Organisation (EPPO) are available to facilitate the procedure of PRA. Recent amendments of these standards allow a better analysis of risks posed by plant pests to biodiversity and the environment. By this, the regulation of species that threaten biodiversity (invasive alien species) can be technically justified according to the Sanitary and Phytosanitary Agreement under the World Trade Organisation. At present, in particular risks of invasive alien plants are in the focus of adapting PRA in Europe. There are several differences between the assessment and management of pests directly harmful to cultivated plants and pests threatening biodiversity or the uncultivated environment. In many cases, the identification of (potential) invasiveness is very difficult. For the assessment of economic importance of environmental risks, several methodologies are provided that differ from the conservative economic assessment. In contrast to the "traditional plant pests", which are introduced unintentionally, alien plants are usually introduced intentionally. Planting them into intended habitats usually does not pose any problems, only very few species spread into unintended habitats and have adverse effects. For selection of management options a differentiated approach is necessary, including the prohibition of introduction of significantly risky plants and the obligation for specified requirements to restrict their spread. Options and difficulties for assessing and managing invasive alien species will be discussed by means of different examples.
App2Check at EMit: Large Language Models for Multilabel Emotion Classification (short paper)

Inthispaperwecomparetheperformanceofthreestate-of-the-artLLM-basedapproachesformultilabelemotionclassification:fine-tunedmultilingualT5andtwofewshotpromptingapproaches:plainFLANandChatGPT.Inourexperimentalanalysis weshowthatFLANT5istheworstperformerandourfine-tunedMT5isthebestperformerinourdevsetand,overall,isbetterthanChatGPT3.5onthetestsetofthecompetition. Moreover,weshowthatMT5andChatGPT3.5havecomplementary performanceondifferentemotionsandthatA2C-best,ourunsubmittedsystemthatcombinesourbestperformermodelsforeachemotion,hasamacroF1thatis0.02greaterthanthewinnerofthecompetitionintheout-of-domainbenchmark. Finally,wesuggestthataperspectivistapproachismoresuitableforevaluatingsystemsonemotiondetection.
Towards Persuasive AI for People with Olfactory Dysfunction

Olfactory dysfunction is a complication arising from various factors, including neurodegenerative disorders, aging, and viral infections like COVID-19. It can adversely affect appetite and food enjoyment, potentially leading to malnutrition. Food tailored to the sensory preferences of such patients can be an effective intervention. However, the utilization of artificial intelligence for suggesting suitable food and persuasive designs for presenting such suggestions has yet to be researched in this context. To address this gap, we conducted a co-creation workshop with patients and multidisciplinary experts to identify food-related goals and challenges faced in daily contexts. The emerging solutions are personalized food recommendations, flavor enhancements, and food sensory descriptors. However, the current databases and models are not suitable for producing these solutions. This is due to the complex feature set required to produce such recommendations. Moreover, such a system needs to incorporate factors such as health, enjoyment, and practical limitations to provide adequate support. Therefore, we outline a research roadmap toward providing personalized and persuasive artificial intelligence for people with olfactory dysfunction.
Eviction and Reception for Description Logic Ontologies (Preliminary Results)

In this work, we consider the problem of modifying a knowledge base in the light of a (set of) models, while preserving the finite representation of the new knowledge base. We analyse the operation of removing models, called eviction, and the operation of adding models, called reception. Given that not all description logics (DLs) are eviction-and reception-compatible, we analyse natural restrictions of the general problem. In particular, we investigate eviction and reception in DL knowledge bases (ontologies), focusing on the very popular ℰℒ language and 𝒜ℒ𝒞 extended with boolean operators over the axioms. First, we extend existing negative results of incompatibility. Then, we place restrictions on the domains of eviction and reception functions on these logics that allows us to recover compatibility.
HaSpeeDe3 at EVALITA 2023: Overview of the Political and Religious Hate Speech Detection task

The Hate Speech Detection (HaSpeeDe3) task is the third edition of a shared task on the detection of hateful content in Italian tweets. It differs from the previous editions while maintaining continuity in analysing and contrasting hate speech (HS) on social media. While HaSpeeDe and HaSpeeDe2 were focused on HS against immigrants, Muslims and Roms, HaSpeeDe3 explores hate speech in strong polarised debates, concerning in particular politics and religion. It is articulated in two different tasks: A) In-domain political hate speech detection and B) Cross-domain hate speech detection about political and religious tweets. Task A consists in two different subtasks for which participants i) can only use the provided textual content of the tweet, or ii) can additionally employ contextual information about the tweet and its author. In Task B, that consists in two subtasks, participants are allowed to use any kind of external data for detecting hate speech in tweets about i) politics and ii) religion. Six teams from both academia and industry participated in the evaluation, with a total of 13 submitted runs for Task A and 16 for Task B.
Generating Haptic Textures with Vibrotactile Under-clothing Wearables

In recent years, immersive technologies consisting of Virtual Reality (VR) and Mixed Reality (MR) are perceiving high interest from various fields. An open challenge in the VR community is to investigate appropriate ways to deliver notifications to users. This paper proposes to use under-clothing wearables to provide vibrotactile feedback on multiple body locations and investigate the generation of haptic textures using wearable devices. Two user studies were conducted to examine the vibrational parameters’ effect on perceived experiences. The first study ( N = 6) aims to investigate the minimal needed force for the different vibration textures to be perceived by users. In the second study, a human-computer interaction researcher ( N = 1) mapped the texture features to physical features, such as softness and sharpness. This study is a first step towards designing multiple haptic textures for wearables that can provide the appropriate experience for a given scenario (e.g., obstacle avoidance and guidance).
BERT_4EVER at LangLearn: Language Development Assessment Model based on Sequential Information Attention Mechanism

In recent years, investigations into language acquisition have greatly benefited from the utilization of natural language processing technologies, particularly in analyzing extensive corpora consisting of authentic texts produced by learners across the realms of first and second language acquisition. A crucial task in this domain involves the assessment of language learners’ language ability development. The “Language Learning Development” task featured in EVALITA 2023 [1] marks a significant milestone as the inaugural shared task focused on automated language development assessment, which entails predicting the relative order of two essays written by the same student. We introduce a novel attention mechanism, namely sequential information attention mechanism, with the primary objective of exploiting information interaction between sequence texts. Experimental results on the COWS dataset show the effectiveness of our proposed sequential information attention mechanism, showcasing its substantial impact on model performance during the final evaluation phase.
Elaboração e avaliação de um Catálogo de Requisitos Não-Funcionais CrÍticos em um Contexto de DomÍnio de Aplicação de Diplomas Digitais

Nos últimos anos, o Ministério da Educação do Brasil instituiu a im-plementação do Diploma Digital nas instituições de educação superior (IES), com o intuito de modernizar o processo de documentações acadêmicas e propor-cionar maior segurança para registro e emissão de diplomas, evitando fraudes. Ao se tratar deste tipo de projeto de software, há a preocupação em definir, im-plementar e gerenciar requisitos não funcionais (RNFs) críticos para garantir a segurança e validade dos Diplomas Digitais, de acordo com as leis e padrões estabelecidos. O levantamento de um catálogo de RNFs críticos seria útil para auxiliar universidades interessadas a desenvolver seu próprio sistema, além de fornecer orientação para o desenvolvimento do software da UNIOESTE, redu-zindo custos para as partes envolvidas. O objetivo principal desta pesquisa é ela-borar e avaliar um catálogo de requisitos não funcionais críticos para o domínio do Diploma Digital, utilizando o NFR Framework.
IUSSNets at DisCoTeX: A fine-tuned approach to coherence

We present our submission to the DisCoTex shared task of the EVALITA 2023 evaluation campaign, which focuses on modeling discourse coherence for Italian texts. We highlight the importance of coherence modeling in natural language processing tasks and briefly discuss related work, including earlier linguistic theories and recent neural models. To tackle the task, we leverage pre-trained Transformer models and fine-tune them on the provided datasets. Our approach incorporates monolingual models due to limited computing resources, but shows potential for multilingual and multitask learning. Our systems ranks second overall, showing that Transformer models can be fruitfully leveraged for coherence assessment, but more work is needed to fully exploit their capabilities. The coherence assessment literature focuses primarily on English; this shared task and our work contribute to broadening the scope of current research.
Experiments with the User's Feedback in Preference Elicitation

This paper deals with user’s preferences (wishes). Common users are uneducated in the decision-making (DM) theory and present their preferences incompletely. That is why we elicit them from such a user during the DM. The paper works with the DM theory called fully probabilistic design (FPD). FPD models closed DM loop, made by the user and the system, by the joint probability density (pd, real pd). A joint ideal pd quantifies the user’s preferences. It assigns high probability values to preferred closed-loop behaviors and low values to undesired behaviors. The real pd should be kept near the ideal pd. By minimizing the Kullback-Leibler divergence of the real and ideal pds, the optimal decision policy is found. The presented algorithmic quantification of preferences provides ambitious but potentially reachable DM aims. It suppresses demands on tuning preference-expressing parameters. The considered ideal pd assigns high probabilities to desired (ideal) sets of states and actions. The parameters of the ideal pd (tuned during the DM via the user’s feedback) are: ▶ relative significance of respective probabilities; ▶ a parameter balancing exploration with exploitation. Their systematic tuning solves meta-DM level task, which observes the agent’s satisfaction expressed humanly by “school-marks”. It opts free parameters to reach the best marks. A formalization and solution of this meta-task were recently done, but experience with it is limited. This paper recalls the theory and provides representative samples of extensive up to now missing simulations.
An Algebraic Notion of Conditional Independence, and its Application to Knowledge Representation (Preliminary Report)

Conditional independence is a crucial concept supporting adequate modelling and efficient reasoning in probabilistics. In knowledge representation, the idea of conditional independence has also been introduced for specific formalisms, such as propositional logic and belief revision. In this paper, the notion of conditional independence is studied in the algebraic framework of approximation fixpoint theory. This gives a language-independent account of conditional independence that can be straightforwardly applied to any logic with fixpoint semantics. It is shown how this notion allows to reduce global reasoning to parallel instances of local reasoning. Furthermore, relations to existing notions of conditional independence are discussed and the framework is applied to normal logic programming.
Collision Prediction with Oncoming Pedestrians on Braille Blocks

This research targets blind people walking on braille blocks. Some non-blind pedestrians walk on braille blocks while they watch their smartphones on the street. Blind people may collide with these oncoming pedestrians. When the oncoming pedestrian does not notice the blind people, a collision will likely occur. We propose a new method for predicting collisions in such situations. We use a smartphone's camera to predict collisions. To predict the collision, we set two conditions. The first condition is whether the oncoming pedestrian is on the collision path. The second condition is whether the oncoming pedestrian notices the blind people.
CLinkaRT at EVALITA 2023: Overview of the Task on Linking a Lab Result to its Test Event in the Clinical Domain

CLinkaRT at EVALITA 2023 is a relation extraction task based on clinical cases taken from the E3C corpus, i.e. Italian written documents reporting statements of a clinical practice. The task consists in identifying clinical results and measures and linking them to the laboratory tests and measurements from which they were obtained. Three teams participated in the task and various supervised machine learning models, both traditional and based on deep learning, were evaluated. In this evaluation, the deep learning models outperformed the traditional ones. Interestingly, none of the teams explored the use of few-shot language modeling. However, the fact that the supervised models significantly outperformed the task baselines implementing few-shot learning shows the crucial role still played by the availability of annotated training data.
Sets Attacking Sets in Abstract Argumentation

In abstract argumentation, arguments jointly attacking single arguments is a well-understood concept, captured by the established notion of SETAFs—argumentation frameworks with collective attacks. In contrast, the idea of sets attacking other sets of arguments has not received much attention so far. In this work, we contribute to the development of set-to-set defeat in formal argumentation. To this end, we introduce so called hyper argumentation frameworks (HYPAFs), a new formalism that extends SETAFs by allowing for set-to-set attacks. We investigate this notion by interpreting these novel attacks in terms of universal, indeterministic, and collective defeat. We will see that universal defeat can be naturally captured by the already existing SETAFs. While this is not the case for indeterministic defeat, we show a close connection to attack-incomplete argumentation frameworks. To formalize our interpretation of collective defeat, we develop novel semantics yielding a natural generalization of attacks between arguments to set-to-set attacks. We investigate fundamental properties and identify several surprising obstacles; for instance, the well-known fundamental lemma is violated, and the grounded extension might not exist. Finally, we investigate the computational complexity of the thereby arising problems.
LangLearn at EVALITA 2023: Overview of the Language Learning Development Task

Language Learning Development (LangLearn) is the EVALITA 2023 shared task on automatic language development assessment, which consists in predicting the evolution of the written language abilities of learners across time. LangLearn is conceived to be multilingual, relying on written productions of Italian and Spanish learners, and representative of L1 and L2 learning scenarios. A total of 9 systems were submitted by 5 teams. The results highlight the open challenges of automatic language development assessment.