Miracle-dz/UniFAR-FTUs huggingface dataset:
Red meat consumption and risk for dyslipidaemia and inflammation: A systematic review and meta-analysis

Aim The study (PROSPERO: CRD42021240905) aims to reveal the relationships among red meat, serum lipids and inflammatory biomarkers. Methods and results PubMed, EMBASE and the Cochrane databases were explored through December 2021 to identify 574 studies about red meat and serum lipids markers including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), C-reactive protein (CRP) or hypersensitive-CRP (hs-CRP). Finally, 20 randomized controlled trials (RCTs) involving 1001 people were included, red meat and serum lipid markers and their relevant information was extracted. The pooled standard mean difference (SMD) was obtained by applying a random-effects model, and subgroup analyses and meta-regression were employed to explain the heterogeneity. Compared with white meat or grain diets, the gross results showed that the consumption of red meat increased serum lipid concentrations like TG (0.29 mmol/L, 95% CI 0.14, 0.44,P<0.001), but did not significantly influence the TC (0.13 mmol/L, 95% CI −0.07, 0.33, P = 0.21), LDL-C (0.11 mmol/L, 95% CI −0.23, 0.45, P = 0.53), HDL-C (−0.07 mmol/L, 95% CI −0.31, 0.17, P = 0.57),CRP or hs-CRP (0.13 mmol/L, 95% CI −0.10, 0.37,P = 0.273). Conclusion Our study provided evidence to the fact that red meat consumption affected serum lipids levels like TG, but almost had no effect on TC, LDL-C, HDL-C and CRP or hs-CRP. Such diets with red meat should be taken seriously to avoid the problem of high lipid profiles. Systematic review registration [https://www.crd. york.ac. uk/PROSPERO], identifier [CRD42021240905].
Influence of the Biosphere on Precipitation: July 1995 Studies with the ARM-CART Data

Ensemble sets of simulation experiments were conducted with a single column model (SCM) using the Goddard GEOS II GCM physics containing a recent version of the Cumulus Scheme (McRAS) and a biosphere based land-fluxes scheme (SSiB). The study used the 18 July to 5 August 1995 ARM-CART (Atmospheric Radiation Measurement-Cloud Atmospheric Radiation Test-bed) data, which was collected at the ARM-CART site in the mid-western United States and analyzed for single column modeling (SCM) studies. The new findings affirm the earlier findings that the vegetation, which increases the solar energy absorption at the surface together with soil and soil-moisture dependent processes, which modulate the surface, fluxes (particularly evapotranspiration) together help to increase the local rainfall. In addition, the results also show that for the particular study period roughly 50% of the increased evaporation over the ARM-CART site would be converted into rainfall with the Column, while the remainder would be advected out to the large-scale. Notwithstanding the limitations of only one-way interaction (i.e., the large-scale influencing the regional physics and not vice versa), the current SCM simulations show a very robust relationship. The evaporation-precipitation relationship turns out to be independent of the soil types, and soil moisture; however, it is weakly dependent on the vegetation cover because of its surface-albedo effect. Clearly, these inferences are prone to weaknesses of the SCM physics, the assumptions of the large-scale being unaffected by gridscale (SCM-scale) changes in moist processes, and other limitations of the evaluation procedures.
Evaluation of a droplet digital PCR assay for quantification of Mycobacterium avium subsp. paratuberculosis DNA in whole-blood and fecal samples from MAP-infected Holstein cattle

Bovine paratuberculosis (PTB) is an infectious disease that affects ruminants worldwide and is a burden on the dairy industry. PTB control measures include culling of Mycobacterium avium subsp. paratuberculosis (MAP)-infected animals from the herd and the enhancement of farm-biosecurity measures. Diagnostics tools for the direct detection of MAP are fecal real-time qPCR and bacteriological culture, the last one being considered the gold standard. However, both show limitations for detecting subclinical MAP-infected cattle with low bacterial load in feces and gut tissues. Droplet digital polymerase chain reaction (ddPCR) is a third-generation PCR method that shows high reproducibility for the quantification of low DNA copy numbers. The objective of this study was to design a ddPCR assay to detect and quantify a fragment of the F57 MAP-specific sequence in samples of naturally MAP-infected Holstein cattle. DNA was isolated from whole-blood and fecal samples from control cows with a negative ELISA and qPCR result (N = 75) and from cows with PTB-associated focal (N = 32), multifocal (N = 21), and diffuse lesions (N = 17) in gut tissues. After ddPCR, the DNA extracted from fecal samples of cows with diffuse lesions showed higher mean copies per microliter (13,791.2 copies/μl) than samples from cows with multifocal lesions (78.8 copies/μl), focal lesions (177.1 copies/μl) or control cows (4.8 copies/μl) (P ≤ 0.05). Significant differences in mean DNA copies/μl were also observed in the blood samples from cows with focal lesions (47.7 copies/μl) when compared with cows with multifocal and diffuse lesions; 18.1 and 12.4 copies/μl, respectively. Using a principal component analysis, the results of the fecal ddPCR clustered together with the results of a commercial ELISA for the specific detection of MAP antibodies, fecal and tissue qPCR, and bacteriological culture results. In contrast, blood ddPCR results clustered together with the results of an ELISA for the detection of a biomarker of subclinical PTB, the ABCA13 transporter. Blood ddPCR was the most sensitive tool (sensitivity 71%, specificity 100%) of all the quantitative methods used in the study for the detection of subclinical cows with focal lesions.
Deep learning techniques for cancer classification using microarray gene expression data

Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer’s effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods.
Production of high brightness H− beams in surface plasma sources

This paper examines topics related to the production of H− beams of maximum brightness in surface‐plasma sources. It is shown that a maximum brightness of ≂2×108 A/cm2 rad2 can be obtained in Penning geometry sources in which efficient generation of negative ions with a reduced energy of ion thermal motion in the plasma of ≤0.5 eV is possible under optimized conditions. In sources with slit optics, the brightness basically is limited by supplementary increase in temperature of ions during beam extraction and formation due to fluctuations of beam intensity. In sources with a round emission aperture, a beam with a brightness of ≂107 A/cm2 rad2 can be produced even when there are significant fluctuations of intensity (≂10–15%).
Applying Gamification Incentives in the Revita Language-learning System

We explore the importance of gamification features in a language-learning platform designed for intermediate-to-advanced learners. Our main thesis is: learning toward advanced levels requires a massive investment of time. If the learner engages in more practice sessions, and if the practice sessions are longer, we can expect the results to be better. This principle appears to be tautologically self-evident. Yet, keeping the learner engaged in general—and building gamification features in particular—requires substantial efforts on the part of developers. Our goal is to keep the learner engaged in long practice sessions over many months—rather than for the short-term. This creates a conflict: In academic research on language learning, resources are typically scarce, and gamification usually is not considered an essential priority for allocating resources. We argue in favor of giving serious consideration to gamification in the language-learning setting—as a means of enabling in-depth research. In this paper, we introduce several gamification incentives in the Revita language-learning platform. We discuss the problems in obtaining quantitative measures of the effectiveness of gamification features.
Spicy Salmon: Converting between 50+ Annotation Formats with Fintan, Pepper, Salt and Powla

Heterogeneity of formats, models and annotations has always been a primary hindrance for exploiting the ever increasing amount of existing linguistic resources for real world applications in and beyond NLP. Fintan - the Flexible INtegrated Transformation and Annotation eNgineering platform introduced in 2020 is designed to rapidly convert, combine and manipulate language resources both in and outside the Semantic Web by transforming it into segmented RDF representations which can be processed in parallel on a multithreaded environment and integrating it with ontologies and taxonomies. Fintan has recently been extended with a set of additional modules increasing the amount of supported non-RDF formats and the interoperability with existing non-JAVA conversion tools, and parts of this work are demonstrated in this paper. In particular, we focus on a novel recipe for resource transformation in which Fintan works in tandem with the Pepper toolset to allow computational linguists to transform their data between over 50 linguistic corpus formats with a graphical workflow manager.
Time Travel in Turkish: WordNets for Modern Turkish

Wordnets have been popular tools for providing and representing semantic and lexical relations of languages. They are useful tools for various purposes in NLP studies. Many researches created WordNets for different languages. For Turkish, there are two WordNets, namely the Turkish WordNet of BalkaNet and KeNet. In this paper, we present new WordNets for Turkish each of which is based on one of the first 9 editions of the Turkish dictionary starting from the 1944 edition. These WordNets are historical in nature and make implications for Modern Turkish. They are developed by extending KeNet, which was created based on the 2005 and 2011 editions of the Turkish dictionary. In this paper, we explain the steps in creating these 9 new WordNets for Turkish, discuss the challenges in the process and report comparative results about the WordNets.
Annotating Japanese Numeral Expressions for a Logical and Pragmatic Inference Dataset

Numeral expressions in Japanese are characterized by the flexibility of quantifier positions and the variety of numeral suffixes. However, little work has been done to build annotated corpora focusing on these features and datasets for testing the understanding of Japanese numeral expressions. In this study, we build a corpus that annotates each numeral expression in an existing phrase structure-based Japanese treebank with its usage and numeral suffix types. We also construct an inference test set for numerical expressions based on this annotated corpus. In this test set, we particularly pay attention to inferences where the correct label differs between logical entailment and implicature and those contexts such as negations and conditionals where the entailment labels can be reversed. The baseline experiment with Japanese BERT models shows that our inference test set poses challenges for inference involving various types of numeral expressions.
Probing the smearing effect by a pointlike graviton in the plane-wave matrix model

We investigate the interaction between a flat membrane and pointlike graviton in the plane-wave matrix model. The one-loop effective potential in the large-distance limit is computed and is shown to be of r{sup -3} type where r is the distance between two objects. This type of interaction has been interpreted as the one incorporating the smearing effect due to the configuration of a flat membrane in a plane-wave background. Our results support this interpretation and provide more evidence about it.
Outreach and Science Communication in the DGS-Korpus Project: Accessibility of Data and the Benefit of Interactive Exchange between Communities

In this paper, we tackle the issues of science communication and dissemination within a sign language corpus project with a focus on spreading accessible information and involving the D/deaf community on various levels. We will discuss successful examples, challenges, and limitations to public relations in such a project and particularly elaborate on use cases. The focus group is presented as a best-practice example of a what we think is a necessary perspective: taking external knowledge seriously and let community experts interact with and provide feedback on a par with academic personnel. Showing both social media and on-site events, we present some exemplary approaches from our team involved in public relations. Keywords: public relations, science communication, sign language community, DGS-Korpus project
A First Attempt at Unreliable News Detection in Swedish

Throughout the COVID-19 pandemic, a parallel infodemic has also been going on such that the information has been spreading faster than the virus itself. During this time, every individual needs to access accurate news in order to take corresponding protective measures, regardless of their country of origin or the language they speak, as misinformation can cause significant loss to not only individuals but also society. In this paper we train several machine learning models (ranging from traditional machine learning to deep learning) to try to determine whether news articles come from either a reliable or an unreliable source, using just the body of the article. Moreover, we use a previously introduced corpus of news in Swedish related to the COVID-19 pandemic for the classification task. Given that our dataset is both unbalanced and small, we use subsampling and easy data augmentation (EDA) to try to solve these issues. In the end, we realize that, due to the small size of our dataset, using traditional machine learning along with data augmentation yields results that rival those of transformer models such as BERT.
Analyzing the Effects of Annotator Gender across NLP Tasks

Recent studies have shown that for subjective annotation tasks, the demographics, lived experiences, and identity of annotators can have a large impact on how items are labeled. We expand on this work, hypothesizing that gender may correlate with differences in annotations for a number of NLP benchmarks, including those that are fairly subjective (e.g., affect in text) and those that are typically considered to be objective (e.g., natural language inference). We develop a robust framework to test for differences in annotation across genders for four benchmark datasets. While our results largely show a lack of statistically significant differences in annotation by males and females for these tasks, the framework can be used to analyze differences in annotation between various other demographic groups in future work. Finally, we note that most datasets are collected without annotator demographics and released only in aggregate form; we call on the community to consider annotator demographics as data is collected, and to release dis-aggregated data to allow for further work analyzing variability among annotators.
Advantages of a Complex Multilayer Annotation Scheme: The Case of the Prague Dependency Treebank

Recently, many corpora have been developed that contain multiple annotations of various linguistic phenomena, from morphological categories of words through the syntactic structure of sentences to discourse and coreference relations in texts. Discussions are ongoing on an appropriate annotation scheme for a large amount of diverse information. In our contribution we express our conviction that a multilayer annotation scheme offers to view the language system in its complexity and in the interaction of individual phenomena and that there are at least two aspects that support such a scheme: (i) A multilayer annotation scheme makes it possible to use the annotation of one layer to design the annotation of another layer(s) both conceptually and in a form of a pre-annotation procedure or annotation checking rules. (ii) A multilayer annotation scheme presents a reliable ground for corpus studies based on features across the layers. These aspects are demonstrated on the case of the Prague Dependency Treebank. Its multilayer annotation scheme withstood the test of time and serves well also for complex textual annotations, in which earlier morpho-syntactic annotations are advantageously used. In addition to a reference to the previous projects that utilise its annotation scheme, we present several current investigations.
NerKor+Cars-OntoNotes++

In this paper, we present an upgraded version of the Hungarian NYTK-NerKor named entity corpus, which contains about twice as many annotated spans and 7 times as many distinct entity types as the original version. We used an extended version of the OntoNotes 5 annotation scheme including time and numerical expressions. NerKor is the newest and biggest NER corpus for Hungarian containing diverse domains. We applied cross-lingual transfer of NER models trained for other languages based on multilingual contextual language models to preannotate the corpus. We corrected the annotation semi-automatically and manually. Zero-shot preannotation was very effective with about 0.82 F1 score for the best model. We also added a 12000-token subcorpus on cars and other motor vehicles. We trained and release a transformer-based NER tagger for Hungarian using the annotation in the new corpus version, which provides similar performance to an identical model trained on the original version of the corpus.
Legal and Ethical Challenges in Recording Air Traffic Control Speech

In this paper the authors detail the various legal and ethical issues faced during the ATCO2 project. This project is aimed at developing tools to automatically collect and transcribe air traffic conversations, especially conversations between pilots and air controls towers. In this paper the authors will develop issues related to intellectual property, public data, privacy, and general ethics issues related to the collection of air-traffic control speech.
D-Terminer: Online Demo for Monolingual and Bilingual Automatic Term Extraction

This contribution presents D-Terminer: an open access, online demo for monolingual and multilingual automatic term extraction from parallel corpora. The monolingual term extraction is based on a recurrent neural network, with a supervised methodology that relies on pretrained embeddings. Candidate terms can be tagged in their original context and there is no need for a large corpus, as the methodology will work even for single sentences. With the bilingual term extraction from parallel corpora, potentially equivalent candidate term pairs are extracted from translation memories and manual annotation of the results shows that good equivalents are found for most candidate terms. Accompanying the release of the demo is an updated version of the ACTER Annotated Corpora for Term Extraction Research (version 1.5).
The 617 MHz–λ 850 μm correlation (cosmic rays and cold dust) in NGC 3044 and NGC 4157

We present the first maps of NGC 3044 and NGC 4157 at $\lambda\,450 \mu$m and $\lambda\,850 \mu$m from the JCMT as well as the first maps at 617 MHz from the GMRT. High latitude emission has been detected in both the radio continuum and sub-mm for NGC 3044 and in the radio continuum for NGC 4157, including several new features. The dust spectrum at long wavelengths required fitting with a two-temperature model for both galaxies, implying the presence of cold dust. Dust masses are $M_d\,=\,1.6\,\times\,10^8 M_\odot$ and $M_d\,=\,2.1\,\times\,10^7 M_\odot$ for NGC 3044 and NGC 4157, respectively, and are dominated by the cold component. There is a clear correlation between the 617 MHz and $\lambda\,850 \mu$m emission in the two galaxies. In the case of NGC 3044, this implies a relation between the non-thermal synchrotron emission and cold dust. The 617 MHz component represents an integration of massive star formation over the past $10^{7-8}$ yr and the $\lambda \,850 \mu$m emission represents heating from the diffuse interstellar radiation field (ISRF). The 617 MHz -- $\lambda\,850 \mu$m correlation improves when a smoothing kernel is applied to the $\lambda\,850 \mu$m data to account for differences between the CR electron diffusion scale and the mean free path of an ISRF photon to dust. The best-fit relation is $L_{617_{\rm MHz}}\,\propto\,{L_{850 \mu{\rm m}}}^{2.1\,\pm\,0.2}$ for NGC 3044. If variations in the cold dust emissivity are dominated by variations in dust density, and the synchrotron emission depends on magnetic field strength (a function of gas density) as well as CR electron generation (a function of massive star formation rate and therefore density via the Schmidt law) then the expected correlation for NGC 3044 is $L_{617_{\rm MHz}}\,\propto\,{L_{850 \mu{\rm m}}}^{2.2}$, in agreement with the observed correlation.
Do machines dream of artificial agreement?

In this paper the (assumed) inconsistency between F1-scores and annotator agreement measures is discussed. This is exemplified in five corpora from the field of argumentation mining. High agreement is important in most annotation tasks and also often deemed important for an annotated dataset to be useful for machine learning. However, depending on the annotation task, achieving high agreement is not always easy. This is especially true in the field of argumentation mining, because argumentation can be complex as well as implicit. There are also many different models of argumentation, which can be seen in the increasing number of argumentation annotated corpora. Many of these reach moderate agreement but are still used in machine learning tasks, reaching high F1-score. In this paper we describe five corpora, in particular how they have been created and used, to see how they have handled disagreement. We find that agreement can be raised post-production, but that more discussion regarding evaluating and calculating agreement is needed. We conclude that standardisation of the models and the evaluation methods could help such discussions.