PISA: A measure of Preference In Selection of Arguments to model verb argument recoverability

Our paper offers a computational model ofthe semantic recoverability of verb arguments,tested in particular on direct objects and In-struments. Our fully distributional model isintended to improve on older taxonomy-basedmodels, which require a lexicon in addition tothe training corpus. We computed the selec-tional preferences of 99 transitive verbs and173 Instrument verbs as the mean value of thepairwise cosine similarity between their argu-ments (a weighted mean between all the argu-ments, or an unweighted mean with the top-mostkarguments).Results show that ourmodel can predict the recoverability of objectsand Instruments, providing a similar result tothat of taxonomy-based models but at a muchcheaper computational cost

A Cloud-Native Web Application for Assisted Metadata Generation and Retrieval: THESPIAN-NER

Within the context of the Competence Centre for the Conservation of Cultural Heritage (4CH) project, the design and deployment of a platform-as-a-service cloud infrastructure for the first European competence centre of cultural heritage (CH) has begun, and some web services have been integrated into the platform. The first integrated service is the INFN-CHNet web application for FAIR storage of scientific analysis on CH: THESPIAN-Mask. It is based on CIDOC-CRM-compatible ontology and CRMhs, describing the scientific metadata. To ease the process of metadata generation and data injection, another web service has been developed: THESPIAN-NER. It is a tool based on a deep neural network for named entity recognition (NER), enabling users to upload their Italian-written report files and obtain labelled entities. Those entities are used as keywords either to serve as (semi)automatically custom queries for the database, or to fill (part of) the metadata form as a descriptor for the file to be uploaded. The services have been made freely available in the 4CH PaaS cloud platform

Strategies to improve the performances of bakery products made from ancient wheat’s

The growing consumers\u2019 attention regarding the inclusion of foods able to provide health benefits in one\u2019s diet, is currently a theme of fundamental importance. Between these products, ancient wheat\u2019s and whole wheat flours seem to be the most appealing in the cereal industry thanks to their nutritional content. Nevertheless, ancient wheat\u2019s show worse rheological and technological performances compared to modern cultivars, in particular when using whole wheat flour. According to Migliorini, et al. (2016), the content of starch and protein is strongly influenced by annual variability and agronomic practices. This highlights the need for further investigation to understand the relationship between different agronomic practices and the rheological and technological properties of flours and dough\u2019s made from ancient wheat\u2019s. Furthermore, the greatest challenge for the bakery industry still remains the improvement of the technological properties of bakery products made from ancient wheat\u2019s. In this paper, some of the strategies aimed to face this challenge are proposed. Starting from the improvement of the rheological properties of dough\u2019s made from ancient wheat, Cappelli, et al. (2018) provided a rheological study which allows to identify the optimal water content to be added, through models represented by level curves diagrams. Moreover, regarding the improvement of bakery products based on ancient wheat, the sourdough fermentation (Saa, et al. 2017) and the reduction of free lipid in the doughs (Collar & Angioloni, 2014) seems to be the most interesting strategies. Finally, future strategies finalized to improve the technological properties of bakery products made from ancient wheat\u2019s are related to the assessment of suitability and bread-making aptitude of ancient wheat flours blended with the most interesting and innovative sources of proteins, i.e. legume and insect flours

Non-Alcoholic Fatty Liver Disease and Risk of Macro- and Microvascular Complications in Patients with Type 2 Diabetes

Non-alcoholic fatty liver disease (NAFLD) is considered the hepatic manifestation of metabolic syndrome. To date, NAFLD is the most frequent chronic liver disease seen day by day in clinical practice across most high-income countries, affecting nearly 25-30% of adults in the general population and up to 70% of patients with T2DM. Over the last few decades, it clearly emerged that NAFLD is a "multisystemic disease" and that the leading cause of death among patients with NAFLD is cardiovascular disease (CVD). Indeed, several observational studies and some meta-analyses have documented that NAFLD, especially its advanced forms, is strongly associated with fatal and non-fatal cardiovascular events, as well as with specific cardiac complications, including sub-clinical myocardial alteration and dysfunction, heart valve diseases and cardiac arrhythmias. Importantly, across various studies, these associations remained significant after adjustment for established cardiovascular risk factors and other confounders. Additionally, several observational studies and some meta-analyses have also reported that NAFLD is independently associated with specific microvascular conditions, such as chronic kidney disease and distal or autonomic neuropathy. Conversely, data regarding a potential association between NAFLD and retinopathy are scarce and often conflicting. This narrative review will describe the current evidence about the association between NAFLD and the risk of macro- and microvascular manifestations of CVD, especially in patients with T2DM. We will also briefly discuss the biological mechanisms underpinning the association between NAFLD and its advanced forms and macro- and microvascular CVD

Predicting gene expression levels from DNA sequences and post-transcriptional information with transformers

Background and objectives: In the latest years, the prediction of gene expression levels has been crucial due to its potential applications in the clinics. In this context, Xpresso and others methods based on Convolutional Neural Networks and Transformers were firstly proposed to this aim. However, all these methods embed data with a standard one-hot encoding algorithm, resulting in impressively sparse matrices. In addition, post-transcriptional regulation processes, which are of uttermost importance in the gene expression process, are not considered in the model.Methods: This paper presents Transformer DeepLncLoc, a novel method to predict the abundance of the mRNA (i.e., gene expression levels) by processing gene promoter sequences, managing the problem as a regression task. The model exploits a transformer-based architecture, introducing the DeepLncLoc method to perform the data embedding. Since DeepLncloc is based on word2vec algorithm, it avoids the sparse matrices problem.Results: Post-transcriptional information related to mRNA stability and transcription factors is included in the model, leading to significantly improved performances compared to the state-of-the-art works. Transformer DeepLncLoc reached 0.76 of R-2 evaluation metric compared to 0.74 of Xpresso.Conclusion: The Multi-Headed Attention mechanisms which characterizes the transformer methodology is suitable for modeling the interactions between DNA's locations, overcoming the recurrent models. Finally, the integration of the transcription factors data in the pipeline leads to impressive gains in predictive power. (C) 2022 Elsevier B.V. All rights reserved

MAFLD and CKD: An Updated Narrative Review

Accumulating evidence now indicates that non-alcoholic fatty liver disease (NAFLD), which is the most common chronic liver disease observed in clinical practice worldwide, is independently associated with an increased risk of incident chronic kidney disease (CKD). Given that NAFLD is linked to insulin resistance, obesity and type 2 diabetes mellitus, an international panel of experts have recently proposed a name change from NAFLD to metabolic associated fatty liver disease (MAFLD). Since the diagnostic criteria for NAFLD and MAFLD are different, observational studies assessing the potential concordance (or even superiority) of MAFLD, compared with NAFLD, in detecting patients at increased risk of hepatic and extra-hepatic complications (including CKD) are required. Hence, in the last two years, some observational studies have investigated the potential relationship between MAFLD and CKD. The result is that, at present, evidence regarding the concordance or even superiority of MAFLD, compared with NAFLD, in detecting patients at higher risk of CKD is still preliminary, although some data indicate that MAFLD identifies patients with CKD as accurately as NAFLD. In this narrative review, we will discuss: (a) the epidemiological evidence assessing the association between NAFLD and risk of incident CKD, (b) the epidemiological data investigating the association between MAFLD and risk of CKD and (c) the biological mechanisms underlying the association between NAFLD/MAFLD and CKD

Autonomous IoT Monitoring Matching Spectral Artificial Light Manipulation for Horticulture

This paper aims at demonstrating the energy self-sufficiency of a LoRaWAN-based sensor node for monitoring environmental parameters exploiting energy harvesting directly coming from the artificial light used in indoor horticulture. A portable polycrystalline silicon module is used to charge a Li-Po battery, employed as the power reserve of a wireless sensor node able to accurately monitor, with a 1-h period, both the physical quantities most relevant for the application, i.e., humidity, temperature and pressure, and the chemical quantities, i.e., O(2) and CO(2) concentrations. To this aim, the node also hosts a power-hungry NDIR sensor. Two programmable light sources were used to emulate the actual lighting conditions of greenhouses, and to prove the effectiveness of the designed autonomous system: a LED-based custom designed solar simulator and a commercial LED light especially thought for plant cultivation purposes in greenhouses. Different lighting conditions used in indoor horticulture to enhance different plant growth phases, obtained as combinations of blue, red, far-red and white spectra, were tested by field tests of the sensor node. The energy self-sufficiency of the system was demonstrated by monitoring the charging/discharging trend of the Li-Po battery. Best results are obtained when white artificial light is mixed with the far-red component, closest to the polycrystalline silicon spectral response peak

Wheat Grain Composition, Dough Rheology and Bread Quality as Affected by Nitrogen and Sulfur Fertilization and Seeding Density

Flour from old varieties are usually considered very weak flours, and thus difficult to use in breadmaking especially when processed as Italian \u201cTipo 2\u201d flour. Hence, the aim of our study was to understand if agronomic treatments can be used to improve flour processability and the quality of three old wheat varieties. An experimental strip-plot scheme was used: three old wheat varieties (Andriolo, Sieve, Verna), two seeding densities, three levels of nitrogen fertilization (N35, N80, and N135), and two levels of foliar sulfur fertilization. Analyzed parameters related to kernel composition, dough rheology and bread quality. Sulfur and nitrogen treatments significantly affected protein composition and dough alveograph strength, which increased by about 34% with nitrogen fertilization, and by about 14% with the sulfur treatment. However, only nitrogen fertilization affected bread characteristics. Crumb density significantly decreased from N35 to N135, while springiness and cohesiveness increased. On the other hand, sulfur did not improve breads. This highlight the importance of performing breadmaking tests in addition to the rheological determinations. The poor technological performance of old wheat flours can be improved with agronomical treatments designed to obtain higher-quality bread