Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic decoupling of biomass and lipid biosynthesis by autonomously regulated switch

For improving the microbial production of fuels and chemicals, gene knock-outs and overexpression are routinely applied to intensify the carbon flow from substrate to product. However, their possibilities in dynamic control of the flux between the biomass and product synthesis are limited, whereas dynamic metabolic switches can be used for optimizing the distribution of carbon and resources. The production of single cell oils is especially challenging, as the synthesis is strongly regulated, competes directly with biomass, and requires defined conditions, such as nitrogen limitation. Here, we engineered a metabolic switch for redirecting carbon flow from biomass to wax ester production in Acinetobacter baylyi ADP1 using acetate as a carbon source. Isocitrate lyase, an essential enzyme for growth on acetate, was expressed under an arabinose inducible promoter. The autonomous downregulation of the expression is based on the gradual oxidation of the arabinose inducer by a glucose dehydrogenase gcd. The depletion of the inducer, occurring simultaneously to acetate consumption, switches the cells from a biomass mode to a lipid synthesis mode, enabling the efficient channelling of carbon to wax esters in a simple batch culture. In the engineered strain, the yield and titer of wax esters were improved by 3.8 and 3.1 folds, respectively, over the control strain. In addition, the engineered strain accumulated wax esters 19% of cell dry weight, being the highest reported among microbes. The study provides important insights into the dynamic engineering of the biomass-dependent synthesis pathways for the improved production of biocompounds from low-cost, sustainable substrates. Significance statement In the biological production, one of the greatest challenges is to find ways for optimal distribution of resources between cell growth, maintenance, and product synthesis. Robust and reliable circuits are required to allow autonomous switching of cells from biomass mode to lipid synthesis mode. Dynamic production of single cell oils such as triacylglycerols and wax esters is especially challenging due to the strong regulation. We present a dynamic genetic circuit based on conditional knockdown of a glyoxylate shunt enzyme, which is essential for cell growth. By gradual repression of the gene, the cells autonomously switch from biomass mode to product synthesis mode. We demonstrate the functionality of the circuit by using bacterium Acinetobacter baylyi ADP1 for the production of long chain alkyl esters, namely wax esters, with titer and yield improved by over 3-fold using acetate as the carbon source.
FHOD‐1 is the only formin in Caenorhabditis elegans that promotes striated muscle growth and Z‐line organization in a cell autonomous manner

The striated body wall muscles of Caenorhabditis elegans are a simple model for sarcomere assembly. Previously, we observed deletion mutants for two formin genes, fhod‐1 and cyk‐1, develop thin muscles with abnormal dense bodies (the sarcomere Z‐line analogs). However, this work left in question whether these formins work in a muscle cell autonomous manner, particularly since cyk‐1(∆) deletion has pleiotropic effects on development. Using a fast acting temperature‐sensitive cyk‐1(ts) mutant, we show here that neither postembryonic loss nor acute loss of CYK‐1 during embryonic sarcomerogenesis cause lasting muscle defects. Furthermore, mosaic expression of CYK‐1 in cyk‐1(∆) mutants is unable to rescue muscle defects in a cell autonomous manner, suggesting muscle phenotypes caused by cyk‐1(∆) are likely indirect. Conversely, mosaic expression of FHOD‐1 in fhod‐1(Δ) mutants promotes muscle cell growth and proper dense body organization in a muscle cell autonomous manner. As we observe no effect of loss of any other formin on muscle development, we conclude FHOD‐1 is the only worm formin that directly promotes striated muscle development, and the effects on formin loss in C. elegans are surprisingly modest compared to other systems.
Clinical scoring is the most sensitive biomarker detecting developing sepsis in newborn mice

Newborn individuals are highly susceptible to infectious diseases. For better insights into age-specific host-pathogen interactions infection models are increasingly employed in neonatal mice. However, for newborn mice no measures are available to objectify the clinical disease state, particularly not in a longitudinal manner, to meet legal animal welfare requirements. We developed a scoring system for newborn mice that relies on observational and examination-based parameters and validated it by applying a Staphylococcus aureus-induced infection model in two different mouse strains. The scoring results strongly correlated with the death kinetics independent of which mouse strain was used. A score above 7 predicted fatality. While the score values increased already at early sepsis stages the large majority of plasma cytokine levels remained comparable to those in uninfected control neonates. The levels of interleukin (Il)-6, chemokine C-C motif ligand 5, Il-1α and tumor necrosis factor α were not increased before 24 hours after infection and correlated only at this late stage of sepsis with the scored disease state. We propose the first clinical scoring system that serves as important research tool to evaluate the clinical course of sepsis in newborn mice. It detects health impairments of newborn pups in a highly sensitive and longitudinal manner, providing information about the disease severity as well as prognosis.
The Influence of Chromosomal Environment on X-Linked Gene Expression in Drosophila melanogaster

Abstract Sex chromosomes often differ from autosomes with respect to their gene expression and regulation. In Drosophila melanogaster, X-linked genes are dosage compensated by having their expression upregulated in the male soma, a process mediated by the X-chromosome-specific binding of the dosage compensation complex (DCC). Previous studies of X-linked gene expression found a negative correlation between a gene’s male-to-female expression ratio and its distance to the nearest DCC binding site in somatic tissues, including head and brain, which suggests that dosage compensation influences sex-biased gene expression. A limitation of the previous studies, however, was that they focused on endogenous X-linked genes and, thus, could not disentangle the effects of chromosomal position from those of gene-specific regulation. To overcome this limitation, we examined the expression of an exogenous reporter gene inserted at many locations spanning the X chromosome. We observed a negative correlation between the male-to-female expression ratio of the reporter gene and its distance to the nearest DCC binding site in somatic tissues, but not in gonads. A reporter gene’s location relative to a DCC binding site had greater influence on its expression than the local regulatory elements of neighboring endogenous genes, suggesting that intra-chromosomal variation in the strength of dosage compensation is a major determinant of sex-biased gene expression. Average levels of sex-biased expression did not differ between head and brain, but there was greater positional effect variation in the brain, which may explain the observed excess of endogenous sex-biased genes located on the X chromosome in this tissue.
Whole mammographic mass segmentation using attention mechanism and multiscale pooling adversarial network

Abstract. Purpose: Since breast mass is a clear sign of breast cancer, its precise segmentation is of great significance for the diagnosis of breast cancer. However, the current diagnosis relies mainly on radiologists who spend time extracting features manually, which inevitably reduces the efficiency of diagnosis. Therefore, designing an automatic segmentation method is urgently necessary for the accurate segmentation of breast masses. Approach: We propose an effective attention mechanism and multiscale pooling conditional generative adversarial network (AM-MSP-cGAN), which accurately achieves mass automatic segmentation in whole mammograms. In AM-MSP-cGAN, U-Net is utilized as a generator network by incorporating attention mechanism (AM) into it, which allows U-Net to pay more attention to the target mass regions without additional cost. As a discriminator network, a convolutional neural network with multiscale pooling module is used to learn more meticulous features from the masses with rough and fuzzy boundaries. The proposed model is trained and tested on two public datasets: CBIS-DDSM and INbreast. Results: Compared with other state-of-the-art methods, the AM-MSP-cGAN can achieve better segmentation results in terms of the dice similarity coefficient (Dice) and Hausdorff distance metrics, achieving top scores of 84.49% and 5.01 on CBIS-DDSM, and 83.92% and 5.81 on INbreast, respectively. Therefore, qualitative and quantitative experiments illustrate that the proposed model is effective and robust for the mass segmentation in whole mammograms. Conclusions: The proposed deep learning model is suitable for the automatic segmentation of breast masses, which provides technical assistance for subsequent pathological structure analysis.
Multicenter experience: early outcome of mitral valve repair in patients with ischemic mitral regurgitation

Background Ischemic mitral regurgitation is associated with poor outcomes. The optimal surgical strategy for management of ischemic mitral regurgitation is still debated. The objective of this study was to evaluate the early mortality and morbidity of mitral valve repair in patients with ischemic mitral regurgitation undergoing coronary artery bypass grafting. Methods We performed a retrospective, observational, cohort study on prospectively collected data on 136 consecutive coronary artery bypass graft patients with ischemic mitral regurgitation undergoing mitral valve repair between January 2016 and January 2020. Perioperative echocardiogram findings, operative procedures, and outcomes were analyzed. Results The overall mortality rate was 4.4%. Mitral valve repair with a low ejection fraction had a 4-fold increase in the risk of death compared to mitral valve repair with preserved ejection fraction > 30%. However, after adjusting for preoperative risk factors, the number of grafts was not an independent risk factor for mortality (odds ratio = 0.18, 95% confidence interval: 0.03–2.81, p = 0.84). Multivariable analysis showed that preoperative ejection fraction (odds ratio = 1.14, 95% confidence interval: 0.82–4.86, p < 0.01), preoperative left ventricular end-systolic dimension (odds ratio = 1.03, 95% confidence interval: 0.65–3.51, p < 0.01) and preoperative left ventricular end-diastolic dimension (odds ratio = 0.99, 95% confidence interval: 0.64–3.28, p = 0.04) were independent risk factors for mortality. Conclusions Mitral valve repair can be performed safely concomitantly with coronary artery bypass grafting in patients with moderate, moderately severe, and severe ischemic mitral regurgitation.
Horizontal orthography versus vertical orthography: The effects of writing direction and syllabic format on visual word recognition in Korean Hangul

The Korean writing system has the flexibility of writing horizontally and vertically as well as two syllabic formats that cannot be found in any other alphabetic script. Consolidating these two characteristics, this study investigated the differential extractions of visual information from the mutilated stimuli of the two syllabic formats of CVC syllables and two writing directions using a lexical decision task. In Experiment 1, the two syllabic formats (i.e., balanced syllables, and vertical syllables, ) were mutilated downwards and upwards (i.e., respectively), along with whole stimuli, in horizontal writing direction. In Experiment 2, the same stimuli and syllabic formats were mutilated rightwards and leftwards (i.e., respectively) in vertical writing direction. Linear mixed effects models showed significant syllabic format effects and writing direction effects, indicating an upper-part superiority in horizontal writing and a right-part advantage in vertical writing. In particular, the right-part superiority in vertical writing is different from a left-part advantage found in Chinese characters. While the upper-part superiority is script-universal, the leftward or rightward bias seems to be script-specific, as readers’ perceptual integration depends on the nature of orthography.
Triple E - Effective Ensembling of Embeddings and Language Models for NER of Historical German

Named entity recognition (NER) for historical texts is a challenging task compared to NER for contemporary texts. Historical texts come with several peculiarities that diﬀer greatly from modern texts and large labeled corpora for training a neural tagger are hardly available. In this work we tackle NER for historical German with an ensembling approach, combining diﬀerent labeled and unlabeled resources of historical and contemporary texts as part of the CLEF HIPE 2020 evaluation lab. We stack diﬀerent word/subword embeddings and transformer-based language models to train a powerful NER tagger for historical German. We conduct experiments with diﬀerent word embeddings, Flair embeddings and pretrained Bert models. The named entities are classiﬁed in literal and in metonymic sense, for which we have developed a separate tagger each. Our experiments show that the usage of Bert is particularly helpful, when trained on a large amount of historical data. Our best ensemble is a combination of FastText embeddings trained on German Wikipedia, Flair embeddings trained on CLEF HIPE data (historical German) and a Bert language model trained on a large corpus of historical German. We release our code and models 3 .
Ensemble of ELECTRA for Profiling Fake News Spreaders

This paper presents an ensemble classiﬁer that uses ELECTRA models for the task of identifying possible Fake News Spreaders on Twitter in PAN at CLEF 2020 lab. Our ensemble is created using 15 models which have been ﬁne-tuned on the task dataset. Our approach scored an accuracy of 0 . 70 and 0 . 69 on the English and Spanish test sets respectively.
Three is Better than One: Ensembling Math Information Retrieval Systems

We report on the systems that the Math Information Retrieval group at Masaryk University (MIRMU) prepared for tasks 1 (find answers) and 2 (formula search) of the ARQMath lab at the CLEF conference. We prototyped three primary MIR systems, proposed several math representations to tackle the lab tasks, and evaluated the proposed systems and representations. We developed a novel algorithm for ensembling information retrieval systems that outperformed all our systems on task 1 and placed ninth out of the 23 competing submissions. Out-of-competition ensembles of all non-baseline primary submissions in the competition made available by the participants placed first on task 1 and third on task 2. Our prototypes will help to understand the challenging problems of answer and formula retrieval in the STEM domain and bring the possibility of accurate math information retrieval one step closer.
Argument Retrieval Using Deep Neural Ranking Models

Conversational argument retrieval is the problem of ranking argumen-tative texts in a collection of focused arguments in order of their relevance to a textual query on different topics. In this notebook-paper for Touché by taking a distant supervision approach for constructing the query relevance information, we investigate seven different deep neural ranking models proposed in the literature with respect to their suitability to this task. In order to incorporate the insights from multiple models into an argument ranking, we further investigate a simple linear aggregation strategy. By retrieving relevant arguments using deep neural ranking models, it will be inspected to what extent the systems whose main concentration is on relevant documents, would be able to retrieve arguments which meet various quality dimensions of the arguments. Our test results suggest that the interaction-focused networks provide better performance compared to the representation-focused networks.
Detecting Fake News Spreaders with Behavioural, Lexical and Psycholinguistic Features

In this paper, we propose a multilingual approach to identifying fake news spreaders on Twitter data, as part of the PAN 2020 competition for author proﬁling. We manually engineered domain-speciﬁc features covering behavioural, lexical and psycholinguistic aspects and evaluated them using traditional machine learning models. The focus of this paper is exploring the problem domain by testing domain-speciﬁc features on different types of classiﬁers ﬁrst, and evaluating a pure multilingual approach on combined English and Spanish texts second. Out of the methods tested, the Gradient Boosting classiﬁers performed best. We extended our experiments to a multilingual design using less preprocessing and feature selection, with a comparable result to the monolingual Gradient Boosting models.
Melaxtech: A report for CLEF 2020 - ChEMU Task of Chemical Reaction Extraction from Patent

This work describes the participation of the Melaxtech team in the CLEF 2020 – ChEMU Task of Chemical Reaction Extraction from Patent. The task consisted of two subtasks: (1) Named entity recognition to identify compounds and different semantic roles in the chemical reaction. (2) Event extraction to identify event-triggers of chemical reaction and their relations with the semantic roles recognized in subtask 1. We developed hybrid approaches combining both deep learning models and pattern-based rules for this task. Our approaches achieved state-of-art re-sults in both subtasks, with the best F1 of 0.957 for entity recognition and the best F1 of 0.9536 for event extraction, indicating the proposed approaches are promising.
BioInfo@UAVR at eRisk 2020: on the Use of Psycholinguistics Features and Machine Learning for the Classification and Quantification of Mental Diseases

This paper describes the participation of the Bioinformatics group of the Institute of Electronics and Engineering Informatics of University of Aveiro in the shared tasks of CLEF eRisk 2020 1 . The eRisk initiative fosters Natural Language Processing research for the automatic detection of risk situations on the internet. Similar to the previous years, the challenge was organized in two tasks, which aimed the early detection of self-harm (T1) and severity of depression (T2) in online forums. We addressed these tasks both from a standard machine learning perspective and from a behavioural point of view. The results we obtained endorse the use of social monitoring as a possible complement to more traditional public health surveillance and intervention practices.
A Multi-Aspect Classification Ensemble Approach for Profiling Fake News Spreaders on Twitter

In this work, we attempt to differentiate authors of fake news and real news as part of the Proﬁling Fake News Spreaders on Twitter task at PAN. We propose a set of eight different language features to represent tweets. These representations are subsequently used in an ensemble classiﬁcation model to identify fake news spreaders on Twitter. The approach is conﬁned to the English language.
Statistical Graph Matching for Indexing Spanish Biomedical Documents

In this work, we describe a statistical graph matching method for semantic indexing of documents from large-scale biomedical repositories in Spanish language provided at the MESINESP 2020 task (8th BioASQ Workshop [15]). The results obtained show enough accurate behavior, especially with respect to the rest of the results in the task. The execution time and computational requirements have been a priority in our approximation, which has proved to be eﬃcient and robust for tackle further improvements.
Hybrid Statistical and Attentive Deep Neural Approach for Named Entity Recognition in Historical Newspapers

Neural networks-based models have proved their eﬃciency on Named Entities Recognition, one of the well-known NLP task. Besides, attention mechanism has become an integral part of compelling sequence modeling and transduction models on various tasks. This technique allows context representation in a sequence by taking into consideration neighboring words. In this study, we propose an architecture that involves BiLSTM layers combined with a CRF layer and an attention layer in between. This was augmented with pre-trained contextualized word embeddings and dropout layers. Moreover, apart from using word representations, we use character-based representations, extracted by CNN layers, to capture morphological and orthographic information. Our experiments show an improvement in the overall performance. We notice that our attentive neural model augmented with contextualized word embeddings gives higher scores compared to our baselines. To the best of our knowledge, there is no study which combines the application of attention mechanism and contextualized word embeddings on NER and historical newspapers.
LasigeBioTM Team at CLEF2020 ChEMU Evaluation Lab: Named Entity Recognition and Event extraction from chemical reactions described in patents using BioBERT NER and RE

This manuscript describes the participation of the Lasige-BioTM team in the NER and EE tasks of the ChEMU evaluation lab. We have ﬁne-tuned the BioBERT NER model to locate and tag named entities and the BioBERT RE model to detect relations between trigger words and named entities. For the NER task, we obtained a F1-score of 0.9392 (exact matching) and 0.9630 (relaxed matching), which was an improvement over the baseline approach and achieving the 3rd best team result. For the EE task, we were not able to produce all the required annotation ﬁles due to the dimension of the test set and, consequently, we did not obtain results in time to submit to the competition. However, we obtained an accuracy of 0.9849 when we applied the BioBERT RE model on the development set.
kdevqa at VQA-Med 2020: Focusing on GLU-based Classification

Interpretation of medical images is a challenging research problem with increasing interest in medical applications of artiﬁcial intelligence. In particular, the ImageCLEF2020 visual question answering (VQA) task is expected to have applications such as a second opinion. The purpose of this research is to ﬁnd an eﬀective VQA-Med system method. We propose neural networks using the Gated Linear Unit for effective fusion of image and question features. Before training, we perform pre-processes and conduct pre-training. We apply so called “inpainting” to remove a logo or text embedded in images so that we attempt to extract image features with less noise. And we use the VQA-Med2019 dataset to train some of the weights of the proposed model. We consider the VQA task as a 332-dimensional classiﬁcation task. The score of our proposed model turns out to be 0.314 in Accuracy and 0.350 in Bleu in VQA-Med2020 task.
SandiDoc at CLEF 2020 - Consumer Health Search : AdHoc IR Task

Information retrieval (IR) processes deal with the retrieval of ranked documents based on the similarity among documents and the key words speciﬁed in user’s query. CLEF 2020 eHealth task on consumer health search adhoc IR addresses the need of improved information retrieval techniques in the health domain to provide relevant information to users. This paper presents our work in CLEF eHealth 2020 consumer health search, on term frequency-inverse document frequency and word vector representation-based techniques adopted in the adhoc IR task. The goal of our work is to experiment on diﬀerent techniques for information retrieval and look into how diﬀerent word vector representations of text can aﬀect the ﬁnal results.