Miracle-dz/UniFAR-FTUs huggingface dataset:
Fighting Fat With Fat: n-3 Polyunsaturated Fatty Acids and Adipose Deposition in Broiler Chickens

Modern broiler chickens are incredibly efficient, but they accumulate more adipose tissue than is physiologically necessary due to inadvertent consequences of selection for rapid growth. Accumulation of excess adipose tissue wastes feed in birds raised for market, and it compromises well-being in broiler-breeders. Studies driven by the obesity epidemic in humans demonstrate that the fatty acid profile of the diet influences adipose tissue growth and metabolism in ways that can be manipulated to reduce fat accretion. Omega-3 polyunsaturated fatty acids (n-3 PUFA) can inhibit adipocyte differentiation, induce fatty acid oxidation, and enhance energy expenditure, all of which can counteract the accretion of excess adipose tissue. This mini-review summarizes efforts to counteract the tendency for fat accretion in broilers by enriching the diet in n-3 PUFA.
Potential Serum Biomarkers for Postoperative Neurocognitive Disorders Based on Proteomic Analysis of Cognitive-Related Brain Regions

Postoperative neurocognitive disorders (po-NCD), including postoperative delirium (POD) and delayed neurocognitive recovery (dNCR), are common in geriatric surgical patients. However, the ideal diagnostic biomarkers to predict individual risks of po-NCDs have not been identified. In this study, proteomic analysis was used to detect dysregulated proteins in three cognitive-related brain regions, the hippocampus, prefrontal cortex, and temporal lobe, of aged dNCR rats. The common affected proteins in these three brain regions were further verified by real-time polymerase chain reaction and western blotting. Furthermore, serum samples from aged rats with dNCR and elderly hip fracture patients with POD were also assessed with enzyme linked immunosorbent assays to investigate the biomarker potential of these dysregulated proteins. The increased expression levels of haptoglobin, caseinolytic protease (ClpP), and alpha-2 macroglobulin (A2M) as well as decreased expression levels of 14-3-3β/α and biliverdin reductase-A (BVR-A) were validated by proteomic analysis in the hippocampus, prefrontal cortex, and temporal lobe of aged dNCR rats. The increased expression of haptoglobin and decreased expression of 14-3-3β/α were further demonstrated in the three brain regions by western blotting. Moreover, increased levels of S100A6 and BVR-A in the hippocampus, S100A6 in the prefrontal cortex, and A2M in the temporal lobe were also observed. More intriguingly, both decreased serum 14-3-3β/α and increased A2M in geriatric POD patients as well as decreased serum ClpP in aged dNCR rats were verified. These results not only indicate potential diagnostic biomarkers for po-NCD but also provide directions for further pathological investigations. Clinical Trial Registration: www.ClinicalTrials. gov, identifier [ChiCTR1900027393].
WVMDA: Predicting miRNA–Disease Association Based on Weighted Voting

An increasing number of experiments had verified that miRNA expression is related to human diseases. The miRNA expression profile may be an indicator of clinical diagnosis and provides a new direction for the prevention and treatment of complex diseases. In this work, we present a weighted voting-based model for predicting miRNA–disease association (WVMDA). To reasonably build a network of similarity, we established credibility similarity based on the reliability of known associations and used it to improve the original incomplete similarity. To eliminate noise interference as much as possible while maintaining more reliable similarity information, we developed a filter. More importantly, to ensure the fairness and efficiency of weighted voting, we focus on the design of weighting. Finally, cross-validation experiments and case studies are undertaken to verify the efficacy of the proposed model. The results showed that WVMDA could efficiently identify miRNAs associated with the disease.
Design, Implementation and Testing of a Network-Based Earthquake Early Warning System in Greece

In this study we implemented and tested the Earthquake Early Warning system PRESTo (PRobabilistic and Evolutionary early warning System, Satriano et al., 2011) on the Greek Ionian islands of Lefkada, Zakynthos and Kefalonia. PRESTo is a free and open source platform for regional Earthquake Early Warning developed at the University of Naples Federico II, which is currently under experimentation in Southern Italy, in the area covered by the Irpinia Seismic Network. The three Ionian islands selected for this study are located on the North-Western part of the Hellenic trench. Here the seismicity rate and the seismic hazard, coupled with the vulnerability of existing critical infrastructures, make this region among the highest seismic risk areas in Europe, where the application of Earthquake Early Warning systems may become a useful strategy to mitigate the potential damage caused by earthquakes. Here we studied the feasibility of implementing an Earthquake Early Warning system on an existing seismic network, which was not specifically made for earthquake early warning purposes, and evaluated the performance of the system, using a data set of real-earthquake recordings. We first describe the technical details of the implementation of PRESTo in the area of interest, including the preliminary parameter configuration and the empirical scaling relationship calibration. Then we evaluated the performance of the system through the off-line analysis of a database of real earthquake records belonging to the most recent M > 4.0 earthquakes occurred in the area. We evaluated the performance in terms of source parameter estimation (location, magnitude), accuracy of ground shaking prediction and lead-time analysis. Finally, we show the preliminary results of the real-time application of PRESTo, performed during the period 01–31 July 2019.
Efficacy and Safety of Adjunctive Aripiprazole, Metformin, and Paeoniae–Glycyrrhiza Decoction for Antipsychotic-Induced Hyperprolactinemia: A Network Meta-Analysis of Randomized Controlled Trials

Aripiprazole, metformin, and paeoniae–glycyrrhiza decoction (PGD) have been widely used as adjunctive treatments to reduce antipsychotic (AP)-induced hyperprolactinemia in patients with schizophrenia. However, the comparative efficacy and safety of these medications have not been previously studied. A network meta-analysis of randomized controlled trials (RCTs) was conducted to compare the efficacy and safety between aripiprazole, metformin, and PGD as adjunctive medications in reducing AP-induced hyperprolactinemia in schizophrenia. Both international (PubMed, PsycINFO, EMBASE, and Cochrane Library databases) and Chinese (WanFang, Chinese Biomedical, and Chinese National Knowledge infrastructure) databases were searched from their inception until January 3, 2019. Data were analyzed using the Bayesian Markov Chain Monte Carlo simulations with the WinBUGS software. A total of 62 RCTs with 5,550 participants were included in the meta-analysis. Of the nine groups of treatments included, adjunctive aripiprazole (<5 mg/day) was associated with the most significant reduction in prolactin levels compared to placebo (posterior MD = −65.52, 95% CI = −104.91, −24.08) and the other eight treatment groups. Moreover, adjunctive PGD (>1:1) was associated with the lowest rate of all-cause discontinuation compared to placebo (posterior odds ratio = 0.45, 95% CI = 0.10, 3.13) and adjunctive aripiprazole (>10 mg/day) was associated with fewer total adverse drug events than placebo (posterior OR = 0.93, 95% CI = 0.65, 1.77) and other eight treatment groups. In addition, when risperidone, amisulpride, and olanzapine were the primary AP medications, adjunctive paeoniae/glycyrrhiza = 1:1, aripiprazole <5 mg/day, and aripiprazole >10 mg/day were the most effective treatments in reducing the prolactin levels, respectively. Adjunctive aripiprazole, metformin, and PGD showed beneficial effects in reducing AP-induced hyperprolactinemia in schizophrenia, with aripiprazole (<5 mg/day) being the most effective one.
Seasonally Related Disruption of Metabolism by Environmental Contaminants in Male Goldfish (Carassius auratus)

Endocrine disrupting chemicals mimic or disrupt action of the natural hormones, adversely impacting hormonal function as well as cardiovascular, reproductive, and metabolic health. Goldfish are seasonal breeders with an annual reproductive cycle regulated by neuroendocrine signaling which involves allocation of metabolic energy to sustain growth and reproduction. We hypothesize that seasonal changes in physiology alter overall vulnerability of goldfish to metabolic perturbation induced by environmental contaminants. In this study, we assess effects of endogenous hormones, individual contaminants and their mixture on metabolism of goldfish at different reproductive stages. Exposure effects were assessed using 1H-NMR metabolomics profiling of male goldfish midbrain, gonad and liver harvested during early recrudescence (October), mid-recrudescence (February) and late recrudescence (June). Compounds assessed include bisphenol A, nonylphenol, bis(2-ethylhexyl) phthalate, fucosterol and a tertiary mixture (DEHP + NP + FS). Metabolome-level responses induced by contaminant exposure across tissues and seasons were benchmarked against responses induced by 17β-estradiol, testosterone and thyroid hormone (T3). We observe a clear seasonal dependence to metabolome-level alteration induced by hormone or contaminant exposures, with February (mid-recrudescence) the stage at which male goldfish are most vulnerable to metabolic perturbation. Responses induced by contaminant exposures differed from those induced by the natural hormones in a season-specific manner. Exposure to the tertiary mixture induced a functional gain at the level of biochemical pathways modeling over responses induced by individual components in select tissues and seasons. We demonstrate the importance of seasonally driven changes in physiology altering overall vulnerability of goldfish to metabolic perturbation induced by environmental contaminants, the relevance of which likely extends to other seasonally-breeding species.
Influence of Microplastics on the Growth and the Intestinal Microbiota Composition of Brine Shrimp

Microplastics (MPs) are ubiquitous in the aquatic environment and can be frequently ingested by zooplankton, leading to various effects. Brine shrimp (Artemia parthenogenetica) has an important role in the energy flow through trophic levels in different seawater systems. In this work, the influence of polyethylene (PE) and polystyrene (PS) MPs on the growth of brine shrimp and corresponding changes of gut microbiota were investigated. Our results showed that the MPs remarkably reduced the growth rate of brine shrimp, and the two types of MPs have different impacts. The average body length of brine shrimps was reduced by 17.92 and 14.95% in the PE group and PS group, respectively. MPs are mainly found in the intestine, and their exposure evidently affects the gut microbiota. By using 16S rRNA gene high-throughput sequencing, 32 phyla of bacteria were detected in the intestine, and the microbiome consisted mainly of Proteobacteria, Firmicutes, and Actinobacteria. MPs’ exposure significantly increased the gut microbial diversity. For the PE group, the proportion of Actinobacteria and Bacteroidetes increased by 45.26 and 2.73%, respectively. For the PS group, it was 54.95 and 1.27%, respectively. According to the analysis on genus level, the proportions of Ponticoccus, Seohaeicola, Polycyclovorans, and Methylophaga decreased by 46.38, 1.24, 1.07, and 2.66%, respectively, for the PE group and 57.87, 1.43, 0.88, and 2.24%, respectively, for the PS group. In contrast, the proportions of Stappia, Microbacterium, and Dietzia increased by 1.12, 23.27, and 11.59%, respectively, for the PE group, and 1.09, 3.79, and 42.96%, respectively, for the PS group. These experimental results demonstrated that the ingestion of MPs by brine shrimp can alter the composition of the gut microbiota and lead to a slow growth rate. This study provides preliminary data support for understanding the biotoxicity of MPs to invertebrate zooplankton and is conducive to the further risk assessment of MP exposure.
Shared Genetics Between Age at Menopause, Early Menopause, POI and Other Traits

Reproductive ageing leading to menopause is characterized by depletion of follicles and its regulating mechanisms are only partly understood. Early age at menopause and premature ovarian insufficiency (POI) are associated with several other traits such as cardiovascular disease, dyslipidemia, osteoporosis and diabetes. In large cohorts of Northern European women hundreds of Single Nucleotide Polymorphisms (SNPs) have been identified to be associated with age at menopause. These SNPs are located in genes enriched for immune and mitochondrial function as well as DNA repair and maintenance processes. Genetic predisposition to earlier menopause might also increase the risk of other associated traits. Increased risk for cardiovascular disease in women has been associated with age at menopause lowering SNPs. Pleiotropy between early age at menopause and increased mortality from coronary artery disease has been observed, implicating that genetic variants affecting age at menopause also affect the risk for coronary deaths. This review will discuss the shared genetics of age at menopause with other traits. Mendelian Randomization studies implicate causal genetic association between age at menopause and age at menarche, breast cancer, ovarian cancer, BMD and type 2 diabetes. Although the shared biological pathways remain to be determined, mechanisms that regulate duration of estrogen exposure remain an important focus.
Gait Phase Recognition Using Fuzzy Logic Regulation with Multisensor Data Fusion

The health challenges brought by aging population and chronic noncommunicable diseases are increasingly severe. Scientific physical exercise is of great significance to prevent the occurrence of chronic diseases and subhealth intervention and promote health. However, improper or excessive exercise can cause injury. Research shows that the sports injury rate of people who often exercise is as high as 85%. Aiming at the problem of low accuracy of single sensor gait analysis, a real-time gait detection algorithm based on piezoelectric film and motion sensor is proposed. On this basis, a gait phase recognition method based on fuzzy logic is proposed, which enhances the ability of gait space-time measurement. Experimental results show that the proposed gait modeling method based on ground reaction force (GRF) signal can effectively recognize and quantify various gait patterns. At the same time, the introduction of heterogeneous sensor data fusion technology can effectively make up for the accuracy defects of single sensor measurement and improve the estimation accuracy of gait space-time measurement.
SINAI at IberLEF-2021 DETOXIS task: Exploring Features as Tasks in a Multi-task Learning Approach to Detecting Toxic Comments

This paper describes the participation of the SINAI research group at DETOXIS (DEtection of TOxicity in comments In Spanish) shared task at IberLEF 2021. The proposed system follows a Multi-task Learning approach where multiple tasks related to toxic comments identiﬁcation are learned in parallel while using a shared representation. Speciﬁcally, we use the dataset features provided by the organizers as tasks along with the combination of polarity classiﬁcation, emotion clas-siﬁcation and oﬀensive language detection tasks to explore if they help in the identiﬁcation of toxic comments. Our proposal ranked ﬁrst in both DETOXIS subtasks, toxicity detection and toxicity level detection.
Influence of Data Dimension Reduction, Feature Scaling and Activation Function on Machine Learning Performance

A dataset containing over 13k samples of dry beans geometric features is being analysed using machine learning (ML) and deep learning (DL) techniques with the goal to automatically classify the bean specie. The obtained geometrical data has quite a lot redundancy. Many features are strongly correlated. This work analyses the influence of data dimension reduction (DDR) (elimination of excess strongly correlated features) and features scaling (FS), often called normalization, on the machine learning performance (measured in terms of accuracy and approximate training time). Additionally also an influence of activation function (sigmoid vs. ReLU) on artificial neural network performance has been checked.
RoMa at HAHA-2021: Deep Reinforcement Learning to Improve a Transformed-based Model for Humor Detection

In this paper, we describe our system we participated in the shared task “Humor Analysis based on Human Annotation (HAHA) at IberLEF-2021 with. Our system relies on data representations learned through ﬁne-tuned neural language models. The representations are used to train a Siamese Neural Network (SNN) which learns to verify whether or not a pair of tweets belong to the same or distinct classes. A key point in our model is the heuristic used to create the pair of messages in the training and test phases. For that, we used a Deep Reinforcement Learning (DRL) strategy that aims at identifying a set of optimal prototypes in each class. In general, the results achieved are encouraging and give us a starting point for further improvements.
WordUp! at VaxxStance 2021: Combining Contextual Information with Textual and Dependency-Based Syntactic Features for Stance Detection

In this paper we describe the participation of the WordUp! team in the VaxxStance shared task at IberLEF 2021. The goal of the competition is to determine the author’s stance from tweets written both in Spanish and Basque on the topic of the Antivaxxers movement. Our approach, in the four di(cid:27)erent tracks proposed, combines the Logistic Regression classi(cid:28)er with diverse groups of features: stylistic, tweet-based, user-based, lexicon-based, dependency-based, and network-based. The outcomes of our experiments are in line with state-of-the-art results on other languages, proving the e(cid:30)cacy of combining methods derived from NLP and Network Science for detecting stance in Spanish and Basque.
Do you need a knowledge graph? Helping organizations determine whether a knowledge graph is needed for their problems with the Smals KG Checklist

A knowledge graph (KG) is a graph that needs to fulfill specific criteria and is key in unlocking data siloes and tacit information for innovative applications. However, organizations may find it hard to identify use cases for KGs and even assessing whether a KGs is a viable approach for a particular problem. Within Smals Research, we designed the Smals KG Checklist, which guides a group of stakeholders in determining whether KG technologies can be used to address a concrete problem. The checklist is to be used in a workshop environment where a facilitator guides the stakeholders in filling the checklist.
PiLN IDPT 2021: Irony Detection in Portuguese Texts with Superficial Features and Embeddings

This paper describes the participation of the PiLN team in the IberLEF 2021 shared task on Irony Detection in Portuguese. The goal of this task is to create a system to detect irony in tweets and news texts. To deal with this topic, we develop a superﬁcial feature-based strategy and an embeddings approach. Moreover, we adopt a back-translation method as data-augmentation to make the tweets Corpus less unbalanced. We evaluate our approaches within several machine learning-based classiﬁers and take the ﬁrst and second places in the tweets Corpus category and seventh and thirteenth places in the news Corpus .
Sentiment Analysis for Spanish Tweets based on Continual Pre-training and Data Augmentation

In this paper, we report the solution of the team BERT4EVER for the sentiment analysis task for Spanish tweets in EmoEvalEs@IberLEF 2021, which aims to classify Spanish tweets into one of the following emotional categories: Anger, Disgust, Fear, Joy, Sadness, Surprise or Others. We adopt the monolingual Spanish BERT model to tackle the problem. In addition, we leverage two augmented strategies to enhance the classic fine-tuned model, namely continual pre-training and data augmentation to improve the generalization capability. Experimental results demonstrate the effectiveness of the BERT model and two augmented strategies.
m-ld: Realtime Information Sharing with RDF

Users of information systems increasingly expect information to be available to edit from multiple devices and by multiple users, online and oﬄine. Strategies exist for shared data types with strong eventual consistency guarantees. These can be complex and fault-prone to implement de novo for application data, and library implementations do not present standard APIs. To improve data interoperability, portability and extensibility, these strategies can be applied to a standard and self-describing data format, RDF. We introduce m-ld , a component providing eventual consistency for RDF data, showing how it can be used to create a collaborative message board program.
Synthesis and computer research of a belt conveyor models with intelligent control

The synthesis problems of intelligent control models for a belt conveyor with a variable lifting angle are considered. The models take into account the acting forces, uneven loading and control actions. A complex criterion of the control quality is developed. The graphs of trajectories are obtained taking into account the selected parameters of the models. An approach to finding the optimal parameters of a model based on artificial intelligence methods is proposed. The conditions for stabilization of the belt conveyor control systems are obtained. Control algorithms are developed based on the construction of fuzzy controllers. The qualitative effects in the models of the belt conveyor control are revealed. Algorithmic and instrumental support is developed for the implementation of software and hardware packages for intelligent control of a belt conveyor with a variable lifting angle. Modules of a software package for modeling of control systems for a belt conveyor using the Python3 language in combination with the Jupyter system and mathematical libraries Numpy, Scipy, Sympy, as well as using open libraries for construction of systems with artificial intelligence are created. Using the developed intelligent algorithms and a high-performance workstation, test examples are studied and a series of computational experiments are carried out. Computational experiments are aimed at finding of control laws and optimal modeling parameters. The obtained results can be used for solving of the enterprises production lines control problems for the innovation cluster of mechanical engineering.
Lessons Learned from 20 Years of Implementing LSI Applications

This paper summarizes lessons learned, over a period of 20 years, from implementing information systems employing the technique of latent semantic indexing (LSI). The data presented is drawn from 63 projects undertaken over the period 1999 through 2019. Over that period the projects increased in scale from collections of hundreds of thousands of documents to ones involving hundreds of millions of documents. They also increased in sophistication, from simple search and retrieval systems to ones focused on information discovery and automated alerting. This paper summarizes some of the key developments in technology and techniques that enabled those advances in the size and sophistication of the applications. The objective of this paper is to share insights gained from these past two decades of system implementation experience.