Miracle-dz/UniFAR-FTUs huggingface dataset:
Reversed upper glycolysis and rapid activation of oxidative pentose phosphate pathway supports the oxidative burst in neutrophils

Neutrophils are abundant white blood cells at the frontline of innate immunity. Upon stimulation, neutrophils rapidly activate effector functions such as the oxidative burst and neutrophil extracellular traps (NETs) to eliminate pathogens. However, little is known about how neutrophil metabolism powers these functions. Our metabolomic analysis on primary human neutrophils revealed that neutrophil metabolism is rapidly rewired upon pro-inflammatory activation, with particularly profound changes observed in glycolysis and the pentose phosphate pathway (PPP). We found that the stimulation-induced changes in PPP were specifically coupled with the oxidative burst. The oxidative burst requires a large amount of NADPH to fuel superoxide production via NADPH Oxidase (NOX). Isotopic tracing studies revealed that in order to maximize the NADPH yield from glucose metabolism, neutrophils quickly adopt near complete pentose cycle during the oxidative burst. In this metabolic mode, all glucose is shunted into the oxidative PPP, and the resulting pentose-phosphate is recycled back to glucose-6-phosphate, which then re-enters the oxidative PPP. To enable this recycling, net flux through the upper glycolytic enzyme glucose-6-phosphate isomerase (GPI) is completely reversed. This allows oxidative PPP flux in neutrophils to reach greater than two-fold of the glucose uptake rate, far exceeding other known mammalian cells and tissues. Intriguingly, the adoption of this striking metabolic mode is completely dependent on an increased demand for NADPH associated with the oxidative burst, as inhibition of NOX resets stimulated neutrophils to use glycolysis-dominant glucose metabolism, with oxidative PPP flux accounting for less than 10% of glucose metabolism. Together, these data demonstrated that neutrophils have remarkable metabolic flexibility that is essential to enable the rapid activation of their effector functions. Graphic Abstract
Cross-lingual annotation: a road map for low- and no-resource languages

This paper presents a “road map” for the annotation of semantic categories in typologically diverse languages, with potentially few linguistic resources, and often no existing computational resources. Past semantic annotation efforts have focused largely on high-resource languages, or relatively low-resource languages with a large number of native speakers. However, there are certain typological traits, namely the synthesis of multiple concepts into a single word, that are more common in languages with a smaller speech community. For example, what is expressed as a sentence in a more analytic language like English, may be expressed as a single word in a more synthetic language like Arapaho. This paper proposes solutions for annotating analytic and synthetic languages in a comparable way based on existing typological research, and introduces a road map for the annotation of languages with a dearth of resources.
Augmenting NLP models using Latent Feature Interpolations

Models with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution. We introduce Emix, a data augmentation method that uses interpolations of word embeddings and hidden layer representations to construct virtual examples. We show that Emix shows significant improvements over previously used interpolation based regularizers and data augmentation techniques. We also demonstrate how our proposed method is more robust to sparsification. We highlight the merits of our proposed methodology by performing thorough quantitative and qualitative assessments.
When Beards Start Shaving Men: A Subject-object Resolution Test Suite for Morpho-syntactic and Semantic Model Introspection

In this paper, we introduce the SORTS Subject-Object Resolution Test Suite of German minimal sentence pairs for model introspection. The full test suite consists of 18,502 transitive clauses with manual annotations of 8 word order patterns, 5 morphological and syntactic and 11 semantic property classes. The test suite has been constructed such that sentences are minimal pairs with respect to a property class. Each property has been selected with a particular focus on its effect on subject-object resolution, the second-most error-prone task within syntactic parsing of German after prepositional phrase attachment (Fischer et al., 2019). The size and detail of annotations make the test suite a valuable resource for natural language processing applications with syntactic and semantic tasks. We use dependency parsing to demonstrate how the test suite allows insights into the process of subject-object resolution. Based on the test suite annotations, word order and case syncretism can be identified as most important factors that affect subject-object resolution.
Heterogeneous Graph Neural Networks to Predict What Happen Next

Given an incomplete event chain, script learning aims to predict the missing event, which can support a series of NLP applications. Existing work cannot well represent the heterogeneous relations and capture the discontinuous event segments that are common in the event chain. To address these issues, we introduce a heterogeneous-event (HeterEvent) graph network. In particular, we employ each unique word and individual event as nodes in the graph, and explore three kinds of edges based on realistic relations (e.g., the relations of word-and-word, word-and-event, event-and-event). We also design a message passing process to realize information interactions among homo or heterogeneous nodes. And the discontinuous event segments could be explicitly modeled by finding the specific path between corresponding nodes in the graph. The experimental results on one-step and multi-step inference tasks demonstrate that our ensemble model HeterEvent[W+E] can outperform existing baselines.
Task-specific roles of local interneurons for inter -and intraglomerular signaling in the insect antennal lobe

Local interneurons (LNs) mediate complex interactions within the antennal lobe, the primary olfactory system of insects, and the functional analog of the vertebrate olfactory bulb. In the cockroach Periplaneta Americana, as in other insects, several types of LNs with distinctive physiological and morphological properties can be defined. Here, we combined whole-cell patch clamp recordings and Ca2+ imaging of individual LNs to analyze the role of spiking and nonspiking LNs in inter- and intraglomerular signaling during olfactory information processing. Spiking GABAergic LNs reacted to odorant stimulation with a uniform rise in [Ca2+]i in the ramifications of all innervated glomeruli. In contrast, in nonspiking LNs, glomerular Ca2+ signals were odorant specific and varied between glomeruli, resulting in distinct, glomerulus-specific tuning curves. The cell type-specific differences in Ca2+ dynamics support the idea that spiking LNs play a primary role in interglomerular signaling, while they assign nonspiking LNs an essential role in intraglomerular signaling.
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models

Embedding of rare and out-of-vocabulary (OOV) words is an important open NLP problem. A popular solution is to train a character-level neural network to reproduce the embeddings from a standard word embedding model. The trained network is then used to assign vectors to any input string, including OOV and rare words. We enhance this approach and introduce an algorithm that iteratively refines and improves both word- and character-level models. We demonstrate that our method outperforms the existing algorithms on 5 word similarity data sets, and that it can be successfully applied to job title normalization, an important problem in the e-recruitment domain that suffers from the OOV problem.
phiC31 integrase for recombination mediated single copy insertion and genome manipulation in C. elegans

C. elegans benefits from a large set of tools for genome manipulation. Yet, the insertion of large DNA constructs and the generation of inversions is still challenging. Here, we adapted the phiC31 integrase system for C. elegans. We generated an integrated phiC31 integrase expressing strain flanked by attP sites that also serves as a landing pad for integration of transgenes by recombination mediated cassette exchange (RCME). This strain is unc-119(-) so RMCE integrants can be produced simply by injection of a plasmid carrying attB sites flanking unc-119(+) and the gene(s) of interest. Additionally, phiC31 integrase is removed concomitantly with integration, eliminating the need to outcross away the integrase. Integrations are relatively easy to obtain for insert sizes up to ~15 kb. Taking advantage of this integration method we establish a dual color fluorescent operon reporter system to study post-transcriptional regulation of mRNA. Last we show that large chromosomal segments can be inverted using phiC31 integrase. Thus the phiC31 integrase system should be a useful addition to the C. elegans toolkit.
XSYSIGMA at SemEval-2020 Task 7: Method for Predicting Headlines’ Humor Based on Auxiliary Sentences with EI-BERT

This paper describes xsysigma team’s system for SemEval 2020 Task 7: Assessing the Funniness of Edited News Headlines. The target of this task is to assess the funniness changes of news headlines after minor editing and is divided into two subtasks: Subtask 1 is a regression task to detect the humor intensity of the sentence after editing; and Subtask 2 is a classification task to predict funnier of the two edited versions of an original headline. In this paper, we only report our implement of Subtask 2. We first construct sentence pairs with different features for Enhancement Inference BERT(EI-BERT)’s input. We then conduct data augmentation strategy and Pseudo-Label method. After that, we apply feature enhancement interaction on the encoding of each sentence for classification with EI-BERT. Finally, we apply weighted fusion algorithm to the logits results which obtained by different pre-trained models. We achieve 64.5% accuracy in subtask2 and rank the first and the fifth in dev and test dataset 1 , respectively.
IRLab_DAIICT at SemEval-2020 Task 12: Machine Learning and Deep Learning Methods for Offensive Language Identification

The paper describes systems that our team IRLab_DAIICT employed for shared task OffensEval2020: Multilingual Offensive Language Identification in Social Media shared task. We conducted experiments on the English language dataset which contained weakly labelled data. There were three sub-tasks but we only participated in sub-tasks A and B. We employed Machine learning techniques like Logistic Regression, Support Vector Machine, Random Forest and Deep learning techniques like Convolutional Neural Network and BERT. Our best approach achieved a MacroF1 score of 0.91 for sub-task A and 0.64 for sub-task B.
Covid or not Covid? Topic Shift in Information Cascades on Twitter

Social media have become a valuable source of information. However, its power to shape public opinion can be dangerous, especially in the case of misinformation. The existing studies on misinformation detection hypothesise that the initial message is fake. In contrast, we focus on information distortion occurring in cascades as the initial message is quoted or receives a reply. We show a significant topic shift in information cascades on Twitter during the Covid-19 pandemic providing valuable insights for the automatic analysis of information distortion.
ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning in Language Using Neuro-symbolic Cloud Computing

I present ETHAN: Experimental Testing of Hybrid AI Node implemented entirely on free cloud computing infrastructure. The ultimate goal of this research is to create modular reusable hybrid neuro-symbolic architecture for Artificial Intelligence. As a test case I model natural language comprehension of causal relations from open domain text corpus that combines semi-supervised language model (Huggingface Transformers) with constituency and dependency parsers (Allen Institute for Artificial Intelligence.)
Cellular Synchronisation through Unidirectional and Phase-Gated Signalling

Multiple natural and artificial oscillator systems achieve synchronisation when oscillators are coupled. The coupling mechanism, essentially the communication between oscillators, is often assumed to be continuous and bidirectional. However, the cells of the presomitic mesoderm synchronise their gene expression oscillations through Notch signalling, which is intermittent and directed from a ligand-presenting to a receptor-presenting cell. Motivated by this mode of communication we present a phase-gated and unidirectional coupling mechanism. We identify conditions under which it can successfully bring two or more oscillators to cycle in-phase. In the presomitic mesoderm we observed the oscillatory dynamics of two synchronizing cell populations and record one population halting its pace while the other keeps undisturbed, as would be predicted from our model. For the same system another important prediction, convergence to a specific range of phases upon synchronisation is also confirmed. Thus, the proposed mechanism accurately describes the coordinated oscillations of the presomitic mesoderm cells and provides an alternative framework for deciphering synchronisation.
Bovine serum albumin as an immunogenic carrier facilitating the development of hapten-specific monoclonal antibodies

There is a common misconception that the generation of hapten-specific monoclonal antibodies (mAbs) requires the use of a heterologous conjugate to ensure carrier-specific antibodies not being detected. In this study, salbutamol (SAL) was used as a model hapten to exhibit the benefits of bovine serum albumin (BSA) as a carrier for developing hapten-specific mAbs. SAL-BSA conjugate would serve as both an immune antigen and a screening antigen during the preparation of SAL-specific mAbs. Six hybridomas were identified to secret mAbs specific for free SAL with minor or negligible cross-reactivity with other β-agonists. Meanwhile, none of hybrodomas secreting anti-BSA antibodies were screened out even though the fetal bovine serum (FBS) added to the medium decreased from 10% to 1% (v/v). Based on one of the six mAbs, 3F12, a direct competitive enzyme-linked immunosorbent assay (dcELISA) was developed for meausring SAL. Under the optimized assay, the quantitative working range was from 312.5 to 20,000 pg/mL (R2 = 0.9959), with a limit of detection (LOD) of 142.9 pg/mL. The results showed that BSA is an efficient and suitable protein carrier for facilitating the development of hapten-specific mAbs.
Comprehensive benchmarking of single cell RNA sequencing technologies for characterizing cellular perturbation

Technological advances in transcriptome sequencing of single cells continues to provide an unprecedented view on tissue composition and cellular heterogeneity. While several studies have compared different single cell RNA-seq methods with respect to data quality and their ability to distinguish cell subpopulations, none of these studies investigated the heterogeneity of the cellular transcriptional response upon a chemical perturbation. In this study, we evaluated the transcriptional response of NGP neuroblastoma cells upon nutlin-3 treatment using the C1, ddSeq and Chromium single cell systems. These devices and library preparation methods are representative for the wide variety of platforms, ranging from microfluid chips to droplet-based systems and from full transcript sequencing to 3-prime end sequencing. In parallel, we used bulk RNA-seq for molecular characterization of the transcriptional response. Two complementary metrics to evaluate performance were applied: the first is the number and identity of differentially expressed genes as defined in consensus by two statistical models, and the second is the enrichment analysis of biological signals. Where relevant, to make the data more comparable, we downsampled sequencing library size, selected cell subpopulations based on specific RNA abundance features, or created pseudobulk samples. While the C1 detects the highest number of genes per cell and better resembles bulk RNA-seq, the Chromium identifies most differentially expressed genes, albeit still substantially fewer than bulk RNA-seq. Gene set enrichment analyses reveals that detection of a limited set of the most abundant genes in single cell RNA-seq experiments is sufficient for molecular phenotyping. Finally, single cell RNA-seq reveals a heterogeneous response of NGP neuroblastoma cells upon nutlin-3 treatment, revealing putative late-responder or resistant cells, both undetected in bulk RNA-seq experiments.
Eosinophil peroxidase induces inflammation in a mouse model of dermatitis

Eosinophils play an important role in mediating itch and inflammation in dermatitis. The role of the eosinophil granule protein eosinophil peroxidase (EPX) in mediating inflammation and itch was tested in a dermatitis mouse model. Mice were sensitized to trimellitic anhydride (TMA) and subsequently challenged chronically on the ear to establish dermatitis. Loss of EPX (in EPX (-/-) mice) or blocking EPX with the drug resorcinol significantly reduced dermatitis in mice exposed to TMA. Resorcinol also reduced levels of thymic stromal lymphopoietin protein (TSLP) in skin. Further studies showed that EPX increased different cytokines in keratinocytes in cell culture via two distinct mechanisms. EPX induced TSLP expression requires lysophosphatidic acid signaling while EPX induced expression of TNF-α, CSF2, CSF3, and IL1α required IL-1 signaling. We also showed that blocking IL-1 reduced inflammation in skin following TMA exposure in mice. Thus, EPX is an important mediator of inflammation and itch, that are mediated via at least two pathways. This suggests that both EPX and its’ signaling pathways may provide novel therapeutic strategies in dermatitis.
Utilization of Co-ZIF as Catalyst for Organic Transformations

An excellent conversions of benzaldehyde was obtained under solvent and solvent free conditions, by utilizing Co(im)20.5DMA as catalyst for the synthesis of benzylidene malononitrile. The catalytic prospect of this crystalline porous framework was extended to oxidation reactions involving ethylene glycol (EG) and ethylbenzene (EB). Unexpectedly, EG was totally oxidized to oxalic acid; which demonstrates the catalyst non-selectivity towards glycolic acid. For EB oxidation under a solvent less condition in the presence of oxygen as oxidant (0.8 Mpa), and a 42 % conversion was achieved with 78.4 % selectivity for acetophenone at 120 °C for a period of 2 h. Keywords— Metal organic frameworks, Zeolitic imidazolate frameworks, Ethylene glycol, Ethylbenzene.
Edition 1.2 of the PARSEME Shared Task on Semi-supervised Identification of Verbal Multiword Expressions

We present edition 1.2 of the PARSEME shared task on identification of verbal multiword expressions (VMWEs). Lessons learned from previous editions indicate that VMWEs have low ambiguity, and that the major challenge lies in identifying test instances never seen in the training data. Therefore, this edition focuses on unseen VMWEs. We have split annotated corpora so that the test corpora contain around 300 unseen VMWEs, and we provide non-annotated raw corpora to be used by complementary discovery methods. We released annotated and raw corpora in 14 languages, and this semi-supervised challenge attracted 7 teams who submitted 9 system results. This paper describes the effort of corpus creation, the task design, and the results obtained by the participating systems, especially their performance on unseen expressions.
Sentiment Forecasting in Dialog

Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation. However, the polarity of next utterance is normally hard to predict, due to the lack of content of next utterance (yet to come). In this study, we propose a Neural Sentiment Forecasting (NSF) model to address inherent challenges. In particular, we employ a neural simulation model to simulate the next utterance based on the context (previous utterances encountered). Moreover, we employ a sequence influence model to learn both pair-wise and seq-wise influence. Empirical studies illustrate the importance of proposed sentiment forecasting task, and justify the effectiveness of our NSF model over several strong baselines.
Schwa-deletion in German noun-noun compounds

We report ongoing research on linking elements in German compounds, with a focus on nounnoun compounds in which the first constituent is ending in schwa. We present a corpus of about 3000 nouns ending in schwa, annotated for various phonological and morpho-syntactic features, and critically, the dominant linking strategy. The corpus analysis is complemented by an unsuccessful attempt to train neural networks and by a pilot experiment asking native speakers to indicate their preferred linking strategy for nonce words. While neither the corpus study nor the experiment offer a clear picture, the results nevertheless provide interesting insights into the intricacies of German compounding. Overall, we find a predominance of the paradigmatic linking element -n. At the same time, the results show that -n is not a default strategy.
A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations

We present a large-scale corpus of e-mail conversations with domain-agnostic and two-level dialogue act (DA) annotations towards the goal of a better understanding of asynchronous conversations. We annotate over 6,000 messages and 35,000 sentences from more than 2,000 threads. For a domain-independent and application-independent DA annotations, we choose ISO standard 24617-2 as the annotation scheme. To assess the difficulty of DA recognition on our corpus, we evaluate several models, including a pre-trained contextual representation model, as our baselines. The experimental results show that BERT outperforms other neural network models, including previous state-of-the-art models, but falls short of a human performance. We also demonstrate that DA tags of two-level granularity enable a DA recognition model to learn efficiently by using multi-task learning. An evaluation of a model trained on our corpus against other domains of asynchronous conversation reveals the domain independence of our DA annotations.
Multi-choice Relational Reasoning for Machine Reading Comprehension

This paper presents our study of cloze-style reading comprehension by imitating human reading comprehension, which normally involves tactical comparing and reasoning over candidates while choosing the best answer. We propose a multi-choice relational reasoning (McR^2) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates. For the fusion representations, we develop an efficient encoding architecture by integrating the schemes of bidirectional attention flow, self-attention and document-gated query reading. Then, comparing and inferring over candidates are executed by a novel relational reasoning network. We conduct extensive experiments on four datasets derived from two public corpora, Children’s Book Test and Who DiD What, to verify the validity and advantages of our model. The results show that it outperforms all baseline models significantly on the four benchmark datasets. The effectiveness of its key components is also validated by an ablation study.