Miracle-dz/UniFAR-FTUs huggingface dataset:
A modular plasmid toolkit applied in marine Proteobacteria reveals functional insights during bacteria-stimulated metamorphosis

A conspicuous roadblock to studying marine bacteria for fundamental research and biotechnology is a lack of modular synthetic biology tools for their genetic manipulation. Here, we applied, and generated new parts for, a modular plasmid toolkit to study marine bacteria in the context of symbioses and host-microbe interactions. To demonstrate the utility of this plasmid system, we genetically manipulated the marine bacterium Pseudoalteromonas luteoviolacea, which stimulates the metamorphosis of the model tubeworm, Hydroides elegans. Using these tools, we quantified constitutive and native promoter expression, developed reporter strains that enable the imaging of host-bacteria interactions, and used CRISPR interference (CRISPRi) to knock down a secondary metabolite and a host-associated gene. We demonstrate the broader utility of this modular system for rapidly creating and iteratively testing genetic tractability by modifying marine bacteria that are known to be associated with diverse host-microbe symbioses. These efforts enabled the successful transformation of twelve marine strains across two Proteobacteria classes, four orders and ten genera. Altogether, the present study demonstrates how synthetic biology strategies enable the investigation of marine microbes and marine host-microbe symbioses with broader implications for environmental restoration and biotechnology.
Transcriptomic-based selection of reference genes for quantitative real-time PCR in an insect endosymbiotic model

Reference genes are a fundamental tool for analyses of gene expression by real-time quantitative PCR (qRT-PCR), in that they ensure the correct comparison between conditions, stages, or treatments. Because of this, selection of appropriate genes to use as references is crucial for proper application of the technique. Nevertheless, efforts to find appropriate, stably expressed transcripts are still lacking, in particular in the field of insect science. Here, we took advantage of a massive transcriptomic high-throughput analysis of various developmental stages of the gut and associated-bacteriomes of the cereal weevil Sitophilus oryzae and identified a subset of stably expressed genes with the potential to be used as housekeeping genes from the larva to the adult stage. We employed several normalization techniques to select the most suitable genes among our subset. Our final selection includes three genes - TAO, YTH3 and PP12A - which can also be used to compare transcript abundance at various developmental stages in symbiotic insects, and in insects devoid of endosymbionts (aposymbiotic). Since they are well conserved, these genes have the potential to be useful for many other insect species. This work confirms the interest in using large-scale, unbiased methods for reference gene selection.
Human Amylin in the Presence of SARS-COV-2 Protein Fragments

COVID-19 can lead to the onset of type-II diabetes, which is associated with the aggregation of islet amyloid polypeptides, also called amylin. Using molecular dynamics simulations, we investigate how the equilibrium between amylin monomers in its functional form and fibrils associated with diabetes is altered in the presence of SARS-COV-2 protein fragments. For this purpose, we study the interaction between the fragment SFYVYSRVK of the envelope protein or the fragment FKNIDGYFKI of the spike protein with the monomer and two amylin fibril models. Our results are compared with earlier work studying such interactions for the two different proteins.
HEXOKINASE-dependent regulation of WRKY transcription factors in Arabidopsis

Sugars are the major product of photosynthesis and provide the stored energy and basic building blocks for all living cells. Sugars also act as dynamic signals throughout the plant life cycle to regulate growth, development and interactions with the biotic and abiotic environment. From a previous RNA-seq experiment, we have identified eight sugar-regulated WRKY transcription factor genes. Focusing on four, we find that WRKY11, WRKY17, WRKY60 and WRKY72 are upregulated by sucrose, glucose or fructose by a superoxide signalling pathway. WRKY gene expression is downregulated by 2-deoxyglucose (2-DG) or mannose, which are inhibitors of hexokinase (HXK), and in hxk1-3 mutants. Mutants in WRKY17, WRKY60 or WRKY72 have reduced hypocotyl growth in response to sucrose, but do not have altered circadian period. Our data suggest that HXK1-dependent regulation of WRKY genes by sugars represents a superoxide-activated transcriptional subnetwork that influences plant growth. Highlight WRKY11, WRKY17, WRKY60 and WRKY72 are upregulated by a sugar-activated superoxide signalling pathway in a HKX1-dependent manner. These sugar-regulated WRKYs represent a transcriptional subnetwork promoting plant growth.
Antibiotic hyper-resistance in a class I aminoacyl-tRNA synthetase with altered active site signature motif

Antibiotics target key biological processes that include protein synthesis. Bacteria respond by developing resistance, which increases rapidly due to antibiotics overuse. Mupirocin, a clinically used natural antibiotic, inhibits isoleucyl-tRNA synthetase (IleRS), an enzyme that links isoleucine to its tRNAIle for protein synthesis. Two IleRSs, mupirocin-sensitive IleRS1 and resistant IleRS2, coexist in bacteria. The latter may also be found in resistant Staphylococcus aureus clinical isolates. Here, we describe the structural basis of mupirocin resistance and unravel a mechanism of hyper-resistance evolved by some IleRS2 proteins. We surprisingly find that an up to 103-fold increase in resistance originates from alteration of the HIGH motif, a signature motif of the class I aminoacyl-tRNA synthetases to which IleRSs belong. The structural analysis demonstrates how an altered HIGH motif could be adopted in IleRS2 but not IleRS1, providing insight into an elegant mechanism for coevolution of the key catalytic motif and associated antibiotic resistance.
Two splice forms of OsbZIP1, a homolog of AtHY5, function to regulate skoto- and photo-morphogenesis in rice

Plants possess well-developed light sensing mechanisms and signal transduction systems for regulating photomorphogenesis. ELONGATED HYOCOTYL 5 (HY5), a basic leucine zipper transcription factor, has been extensively characterized in dicot plants. In this study, we have shown that OsbZIP1 is a functional homolog of Arabidopsis HY5 (AtHY5) and is important for light-mediated regulation of seedling and mature plant development in rice. Ectopic expression of OsbZIP1 in rice reduces plant height and leaf length without affecting plant fertility, which is in contrast to OsbZIP48, another HY5 homolog we characterised earlier. OsbZIP1 is alternatively spliced and the isoform OsbZIP1.2 lacking COP1 binding domain regulates seedling development in dark; this is unique since AtHY5 lacking COP1 binding domain does not display such a phenotype. Rice seedlings overexpressing OsbZIP1 were found to be shorter than vector control under white and monochromatic light conditions whereas RNAi seedlings displayed completely opposite phenotype. While OsbZIP1.1 is light regulated, OsbZIP1.2 shows similar protein profile in both light and dark conditions. Due to its interaction with OsCOP1, OsbZIP1.1 undergoes degradation via 26S proteasome under dark conditions. Also, OsbZIP1.1 interacts with CASEIN KINASE 2 (OsCK2ɑ3) and consequently undergoes phosphorylation. In comparison, OsbZIP1.2 did not show any interaction with COP1 and OsCK2ɑ3. We propose that OsbZIP1.1 most likely works under low fluence of blue light (15 μmol/m²/s) while OsbZIP1.2 becomes dominant as the fluence is increased to 30 μmol/m²/s. Data presented in this study reveal that AtHY5 homologs in rice have undergone neofunctionalization and alternative splicing (AS) of OsbZIP1 has increased the repertoire of its functions. One sentence summary: Alternative spliced forms of OsbZIP1, an AtHY5 homolog in rice, regulate seedling development in response to light and dark
DTX3L and USP28 fine-tune DNA double strand repair through mutual regulation of their protein levels

The DNA damage response involves a complex protein network with members mediating different post-translational modifications such as ubiquitination and deubiquitination. Thereby the E3 ubiquitin ligase DTX3L as well as the deubiquitinase USP28 are recruited especially to DNA double strand breaks (DSBs) suggesting mutual functional interactions. Here we present evidence for the existence of such crosstalk. Mechanistically we show that DTX3L interacts with USP28 and ubiquitinates it, which leads to its proteasomal degradation. Vice versa, USP28 can remove those polyubiquitin chains from itself as well as from autoubiquitinated DTX3L. Consequently, these mutual regulatory interactions between DTX3L and USP28 affected DSB repair activities. Analysis of distinct DSB repair pathways reveals synthetic dysfunction of canonical non-homologues end joining (NHEJ) and homologous recombination (HR), upon USP28 and DTX3L double knockdown, suggesting cooperation between these proteins. Conversely, error-prone microhomology-mediated end joining (MMEJ) requires USP28 to counterbalance the antagonistic DTX3L effect. Together, the present data indicate that DTX3L and USP28 are under mutual control to fine-tune the capacity and quality of the cellular responses to stresses such as DNA damage.
Trophic eggs affect caste determination in the ant Pogonomyrmex rugosus

Understanding how a single genome creates distinct phenotypes remains a fundamental challenge for biologists. Social insects provide a striking example of polyphenism, with queen and worker castes exhibiting morphological, behavioural, and reproductive differences. Here we show that trophic eggs, which do not contain an embryo and are primarily regarded as a source of food, play a role in the process of caste determination in the harvester ant Pogonomyrmex rugosus. When first instar larvae were given access to trophic eggs, they mostly developed into workers. By contrast, larvae without access to trophic eggs developed into queens. We found that trophic eggs differ in many ways from reproductive eggs, including texture, morphology and their contents of protein, triglycerides, glycogen, sugar and small RNAs. Moreover, comparison of miRNA fragment size distributions suggests differences in the composition of miRNAs between the two egg types. This is the first demonstration of trophic eggs playing a role in caste determination in social insects.
Co-evolutionary patterns shown in Ostreococcus-virus system from the Western Baltic Sea in freshly isolated hosts and viruses

Marine viruses are a major driver of phytoplankton mortality and thereby influence biogeochemical cycling of carbon and other nutrients. In recent years, an understanding of the potential importance of phytoplankton-targeting viruses on ecosystem dynamics has emerged, but experimental investigations of host-virus interactions on a broad spatial and temporal scale are still missing. Here, we investigated in detail a phytoplankton host’s responses reacting to infections by species-specific viruses from i) distinct geographical regions and ii) different sampling seasons. Specifically, we used two species of picophytoplankton (1 µm) Ostreococcus tauri and O. mediterraneus and their viruses (size ca. 100 nm), which represent systems well-known in marine biology, but almost entirely ignored in evolutionary biology. The strains stem from different regions of the Southwestern Baltic Sea that vary in salinity and temperature. Using an experimental cross-infection set-up, we show that in this natural system evolutionary history, and thus the timing of when hosts and their associated viruses coexisted, was the main driver of infection patterns. In addition species and strain specificity underline the present understanding of rapid host-virus co-evolution.
Most NLG is Low-Resource: here’s what we can do about it

Many domains and tasks in natural language generation (NLG) are inherently ‘low-resource’, where training data, tools and linguistic analyses are scarce. This poses a particular challenge to researchers and system developers in the era of machine-learning-driven NLG. In this position paper, we initially present the challenges researchers & developers often encounter when dealing with low-resource settings in NLG. We then argue that it is unsustainable to collect large aligned datasets or build large language models from scratch for every possible domain due to cost, labour, and time constraints, so researching and developing methods and resources for low-resource settings is vital. We then discuss current approaches to low-resource NLG, followed by proposed solutions and promising avenues for future work in NLG for low-resource settings.
Experimental Machine Translation of the Swiss German Sign Language via 3D Augmentation of Body Keypoints

This paper describes the participation of DFKI-SLT at the Sign Language Translation Task of the Seventh Conference of Machine Translation (WMT22). The system focuses on the translation direction from the Swiss German Sign Language (DSGS) to written German. The original videos of the sign language were analyzed with computer vision models to provide 3D body keypoints. A deep-learning sequence-to-sequence model is trained on a parallel corpus of these body keypoints aligned to written German sentences. Geometric data augmentation occurs during the training process. The body keypoints are augmented by artificial rotation in the three dimensional space. The 3D-transformation is calculated with different angles on every batch of the training process.
Gender Bias Mitigation for NMT Involving Genderless Languages

It has been found that NMT systems have a strong preference towards social defaults and biases when translating certain occupations, which due to their widespread use, can unintentionally contribute to amplifying and perpetuating these patterns. In that sense, this work focuses on sentence-level gender agreement between gendered entities and occupations when translating from genderless languages to languages with grammatical gender. Specifically, we address the Basque to Spanish translation direction for which bias mitigation has not been addressed. Gender information in Basque is explicit in neither the grammar nor the morphology. It is only present in a limited number of gender specific common nouns and person proper names. We propose a template-based fine-tuning strategy with explicit gender tags to provide a stronger gender signal for the proper inflection of occupations. This strategy is compared against systems fine-tuned on real data extracted from Wikipedia biographies. We provide a detailed gender bias assessment analysis and perform a template ablation study to determine the optimal set of templates. We report a substantial gender bias mitigation (up to 50% on gender bias scores) while keeping the original translation quality.
Learning to Generate Overlap Summaries through Noisy Synthetic Data

Semantic Overlap Summarization (SOS) is a novel and relatively under-explored seq-to-seq task which entails summarizing common information from multiple alternate narratives. One of the major challenges for solving this task is the lack of existing datasets for supervised training. To address this challenge, we propose a novel data augmentation technique, which allows us to create large amount of synthetic data for training a seq-to-seq model that can perform the SOS task. Through extensive experiments using narratives from the news domain, we show that the models fine-tuned using the synthetic dataset provide significant performance improvements over the pre-trained vanilla summarization techniques and are close to the models fine-tuned on the golden training data; which essentially demonstrates the effectiveness of out proposed data augmentation technique for training seq-to-seq models on the SOS task.
Papago’s Submission to the WMT22 Quality Estimation Shared Task

This paper describes anonymous submission to the WMT 2022 Quality Estimation shared task. We participate in Task 1: Quality Prediction for both sentence and word-level quality prediction tasks. Our system is a multilingual and multi-task model, whereby a single system can infer both sentence and word-level quality on multiple language pairs. Our system’s architecture consists of Pretrained Language Model (PLM) and task layers, and is jointly optimized for both sentence and word-level quality prediction tasks using multilingual dataset. We propose novel auxiliary tasks for training and explore diverse sources of additional data to demonstrate further improvements on performance. Through ablation study, we examine the effectiveness of proposed components and find optimal configurations to train our submission systems under each language pair and task settings. Finally, submission systems are trained and inferenced using K-folds ensemble. Our systems greatly outperform task organizer’s baseline and achieve comparable performance against other participants’ submissions in both sentence and word-level quality prediction tasks.
Developing a Tag-Set and Extracting the Morphological Lexicons to Build a Morphological Analyzer for Egyptian Arabic

This paper sheds light on an in-progress work for building a morphological analyzer for Egyptian Arabic (EGY). To build such a tool, a tag-set schema is developed depending on a corpus of 527,000 EGY words covering different sources and genres. This tag-set schema is used in annotating about 318,940 words, morphologically, according to their contexts. Each annotated word is associated with its suitable prefix(s), original stem, tag, suffix(s), glossary, number, gender, definiteness, and conventional lemma and stem. These morphologically annotated words, in turns, are used in developing the proposed morphological analyzer where the morphological lexicons and the compatibility tables are extracted and tested. The system is compared with one of best EGY morphological analyzers; CALIMA.
SQU-CS @ NADI 2022: Dialectal Arabic Identification using One-vs-One Classification with TF-IDF Weights Computed on Character n-grams

In this paper, I present an approach using one-vs-one classification scheme with TF-IDF term weighting on character n-grams for identifying Arabic dialects used in social media. The scheme was evaluated in the context of the third Nuanced Arabic Dialect Identification (NADI 2022) shared task for identifying Arabic dialects used in Twitter messages. The approach was implemented with logistic regression loss and trained using stochastic gradient decent (SGD) algorithm. This simple method achieved a macro F1 score of 22.89% and 10.83% on TEST A and TEST B, respectively, in comparison to an approach based on AraBERT pretrained transformer model which achieved a macro F1 score of 30.01% and 14.84%, respectively. My submission based on AraBERT scored a macro F1 average of 22.42% and was ranked 10 out of the 19 teams who participated in the task.
Understanding Narratives from Demographic Survey Data: a Comparative Study with Multiple Neural Topic Models

Fertility intentions as verbalized in surveys are a poor predictor of actual fertility outcomes, the number of children people have. This can partly be explained by the uncertainty people have in their intentions. Such uncertainties are hard to capture through traditional survey questions, although open-ended questions can be used to get insight into people’s subjective narratives of the future that determine their intentions. Analyzing such answers to open-ended questions can be done through Natural Language Processing techniques. Traditional topic models (e.g., LSA and LDA), however, often fail to do since they rely on co-occurrences, which are often rare in short survey responses. The aim of this study was to apply and evaluate topic models on demographic survey data. In this study, we applied neural topic models (e.g. BERTopic, CombinedTM) based on language models to responses from Dutch women on their fertility plans, and compared the topics and their coherence scores from each model to expert judgments. Our results show that neural models produce topics more in line with human interpretation compared to LDA. However, the coherence score could only partly reflect on this, depending on the corpus used for calculation. This research is important because, first, it helps us develop more informed strategies on model selection and evaluation for topic modeling on survey data; and second, it shows that the field of demography has much to gain from adopting NLP methods.
Prospectus Language and IPO Performance

Pricing a firm’s Initial Public Offering (IPO) has historically been very difficult, with high average returns on the first-day of trading. Furthermore, IPO withdrawal, the event in which companies who file to go public ultimately rescind the application before the offering, is an equally challenging prediction problem. This research utilizes word embedding techniques to evaluate existing theories concerning firm sentiment on first-day trading performance and the probability of withdrawal, which has not yet been explored empirically. The results suggest that firms attempting to go public experience a decreased probability of withdrawal with the increased presence of positive, litigious, and uncertain language in their initial prospectus, while the increased presence of strong modular language leads to an increased probability of withdrawal. The results also suggest that frequent or large adjustments in the strong modular language of subsequent filings leads to smaller first-day returns.
Argument Mining for Review Helpfulness Prediction

The importance of reliably determining the helpfulness of product reviews is rising as both helpful and unhelpful reviews continue to accumulate on e-commerce websites. And argumentational features—such as the structure of arguments and the types of underlying elementary units—have shown to be promising indicators of product review helpfulness. However, their adoption has been limited due to the lack of sufficient resources and large-scale experiments investigating their utility. To this end, we present the AMazon Argument Mining (AM^2) corpus—a corpus of 878 Amazon reviews on headphones annotated according to a theoretical argumentation model designed to evaluate argument quality.Experiments show that employing argumentational features leads to statistically significant improvements over the state-of-the-art review helpfulness predictors under both text-only and text-and-image settings.
The influence of solid core on Earth's hydrodynamics

Abstract A solution of the momentum equation in the outer fluid core of the Earth is obtained, in presence of inner solid core which includes its rotation rate. It is proposed to use this solution for development of a computer program for numerical solution of the generation problem. An advantage of such a scheme, which distinguishes explicitly the boundary layers, should be the increased speed of calculations and smaller values of the Ekman number. We believe that the expressions for the meridional convection obtained here will induce development of a model of the mass-transfer in the core. This would allow to give up the model with prescribed Archimedean velocity, used widely nowadays, in favor of more realistic models in which the latter is a result of the solution of the problem.