Miracle-dz/UniFAR-FTUs huggingface dataset:
ApoE4 disrupts interaction of sortilin with fatty acid-binding protein 7 essential to promote lipid signaling

Sortilin is a receptor for neuronal uptake of apolipoprotein E. Sortilin-dependent uptake of lipidated apoE promotes conversion of polyunsaturated fatty acids (PUFA) into neuromodulators that induce anti-inflammatory gene expression in the brain. This neuroprotective pathway works with apoE3 but is lost with apoE4, the main risk factor for Alzheimer’s disease (AD). Here, we elucidated steps in cellular handling of lipids through sortilin, and why they are disrupted by apoE4. Combining unbiased proteome screens with analyses in mouse models, we uncover interaction of sortilin with fatty acid-binding protein (FABP) 7, the intracellular carrier for PUFA in the brain. In the presence of apoE3, sortilin promotes functional expression of FABP7 and its ability to elicit lipid-dependent gene transcription. By contrast, apoE4 binding blocks sortilin sorting, causing catabolism of FABP7 and impairing lipid signaling. Reduced FABP7 levels in the brain of AD patients expressing apoE4 substantiate the relevance of these interactions for neuronal lipid homeostasis. Taken together, we document interaction of sortilin with mediators of extracellular and intracellular lipid transport that provides a mechanistic explanation for loss of a neuroprotective lipid metabolism in AD. SUMMARY STATEMENT Lipids are central to brain health and defects in brain lipid homeostasis are causal to neurodegenerative processes in Alzheimer’s disease. Here, we uncovered how the neuronal lipoprotein receptor sortilin interacts with apoE and FABP7, the carriers for extra- and intracellular transport of lipids in the brain, respectively. We show that this interaction enables lipids to control gene transcription via nuclear receptors; and why this presumed neuroprotective lipid action is disturbed in humans who carry the ε4 variant of apoE, the most important risk factor for sporadic Alzheimer’s disease.
DnaA and SspA Regulation of the iraD gene of E. coli: an alternative DNA damage response independent of LexA/RecA

The transcription factor RpoS (σS) of Escherichia coli controls a large number of genes important for tolerance of a variety of stress conditions. IraD promotes the post-translation stability of RpoS by inhibition of RssB, an adaptor protein for ClpXP degradation. We have previously documented DNA damage induction of iraD expression, independent of the SOS response. Both iraD and rpoS are required for tolerance to DNA damaging treatments such as H2O2 and the replication inhibitor azidothymidine in the log phase of growth. Using luciferase gene fusions to the 672 bp iraD upstream region, we show here that both promoters of iraD are induced by AZT. Genetic analysis suggests that both promoters are repressed by DnaA-ATP, partially dependent on a putative DnaA box at −81 bp, and regulated by RIDA (regulatory inactivation of DnaA), dependent on the DnaN processivity clamp. By electrophoretic mobility shift assays we show that purified DnaA protein binds to the iraD upstream region, so DnaA regulation of IraD is likely to be direct. DNA damage induction of iraD during log phase growth is abolished in the dnaA-T174P mutant, suggesting that DNA damage, in some way, relieves DnaA repression, possibly through the accumulation of replication clamps and enhanced RIDA. We demonstrate that the RNA-polymerase associated factor, SspA (stringent starvation protein A), induced by the accumulation of ppGpp, also affects IraD expression, with a positive effect on constitutive expression and a negative effect on AZT-induced expression, in a fashion independent of DnaA. SIGNIFICANCE DNA damage can lead to cell death or genomic instability. Cells have evolved transcriptional responses that sense DNA damage and up-regulate tolerance and repair factors; the LexA/RecA-regulated SOS response in E. coli was the first example of such a system. This work describes an alternative DNA damage response, controlled by DnaA and the IraD post-translational regulator of RpoS. The cellular signals for this response, we propose, are empty replication processivity (β) clamps that accumulate at replication blocks. IraD expression is also regulated by stringent starvation protein, SspA, induced by nutrient deprivation. This SOS-independent DNA damage response integrates a signal of incomplete replication with starvation to modulate expression of genes that promote the completion of replication.
Generative adversarial neural networks maintain decoder accuracy during signal disruption in simulated long-term recordings from brain computer interfaces

Chronically implanted brain-computer interfaces (BCIs) provide amazing opportunities to those living with disability and for the treatment of chronic disorders of the nervous system. However, this potential has yet to be fully realized in part due to the lack of stability in measured signals over time. Signal disruption stems from multiple sources including mechanical failure of the interface, changes in neuron health, and glial encapsulation of the electrodes that alter the impedance. In this study we present an algorithmic solution to the problem of long-term signal disruption in chronically implanted neural interfaces. Our approach utilizes a generative adversarial network (GAN), based on the original Unsupervised Image to Image Translation (UNIT) algorithm, which learns how to recover degraded signals back to their analogous non-disrupted (“clean”) exemplars measured at the time of implant. We demonstrate that this approach can reliably recover simulated signals in two types of commonly used neural interfaces: multi-electrode arrays (MEA), and electrocorticography (ECoG). To test the accuracy of signal recovery we employ a common BCI paradigm wherein a classification algorithm (neural decoder) is trained on the starting (non-disrupted) set of signals. Performance of the decoder demonstrates expected failure over time as the signal disruption accumulates. In simulated MEA experiments, our approach recovers decoder accuracy to >90% when as many as 13/ 32 channels are lost, or as many as 28/32 channels have their neural responses altered. In simulated ECoG experiments, our approach shows stabilization of the neural decoder indefinitely with decoder accuracies >95% over simulated lifetimes of over 1 year. Our results suggest that these types of neural networks can provide a useful tool to improve the long-term utility of chronically implanted neural interfaces.
Continuous Seizure Emergency Evoked in Mice with Pharmacological, Electrographic, and Pathological Features Distinct from Status Epilepticus

Objectives Benzodiazepines are the standard of care for the management of sustained seizure emergencies, including status epilepticus (SE) and seizure clusters. Seizure clusters are a variably defined seizure emergency wherein a patient has multiple seizures above a baseline rate, with intervening periods of recovery, distinguishing clusters from SE. While phenotypically distinct, the precise pathophysiological and mechanistic differences between SE and seizure clusters are under studied. Preclinical interrogation is needed to help uncover the behavioral, physiological, and pathological mechanisms associated with seizure emergencies in order to better manage these events in the susceptible individual. Methods Herein, we characterize a novel model of sustained seizure emergency induced in CF-1 mice through the combined administration of high-dose phenytoin (PHT; 50 mg/kg, i.p.) and pentylenetetrazol (PTZ; 100 mg/kg, s.c.). Results In the present manuscript we describe a mouse model of sustained seizure emergency that is physiologically, pharmacologically, and histologically distinct from SE. Acute administration of PHT 1 hour prior to s.c. PTZ led to significantly more mice with continuous seizure activity (CSA; 73.4%) versus vehicle-pretreated mice (13.8%; p<0.0001). CSA was sensitive to lorazepam and valproic acid when administered at seizure onset, as well as 30-minutes post-seizure onset. Carbamazepine worsened seizure control and post-CSA survival. Mice in CSA exhibited EEG patterns distinct from kainic acid-induced SE and s.c. PTZ alone, clearly differentiating CSA from SE and s.c. PTZ-induced myoclonic seizures. Neuropathological assessment by FluoroJade-C staining of brains collected 24-hours later revealed no neurodegeneration in any mice with CSA, whereas there was widespread neuronal death in brains from KA-SE mice. Significance This study defines a novel mouse model on which to elucidate the mechanistic differences between sustained seizure emergencies (i.e. SE and seizure clusters) to improve discovery of effective clinical interventions and define mechanisms of seizure termination. Key Points Box Seizure clusters are a variably defined seizure emergency that is sensitive to benzodiazepines, distinct from status epilepticus. The mechanistic differences between seizure clusters and status epilepticus are not well defined. We report a mouse seizure emergency model that is phenotypically, pathologically, and pharmacologically distinct from status epilepticus. This mouse model provides a novel platform on which to further interrogate the mechanisms underlying seizure emergencies.
MIMOQA: Multimodal Input Multimodal Output Question Answering

Multimodal research has picked up significantly in the space of question answering with the task being extended to visual question answering, charts question answering as well as multimodal input question answering. However, all these explorations produce a unimodal textual output as the answer. In this paper, we propose a novel task - MIMOQA - Multimodal Input Multimodal Output Question Answering in which the output is also multimodal. Through human experiments, we empirically show that such multimodal outputs provide better cognitive understanding of the answers. We also propose a novel multimodal question-answering framework, MExBERT, that incorporates a joint textual and visual attention towards producing such a multimodal output. Our method relies on a novel multimodal dataset curated for this problem from publicly available unimodal datasets. We show the superior performance of MExBERT against strong baselines on both the automatic as well as human metrics.
A Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents

Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is substantially challenging. First, the users are infrequent callers who are unfamiliar with the expectations of a pre-designed conversation flow. Second, the users are paying customers of an enterprise who demand a reliable, consistent and efficient user experience when resolving their issues. In this work, we describe a general and robust framework for intent and entity extraction utilizing a hybrid of statistical and rule-based approaches. Our framework includes confidence modeling that incorporates information from all components in the SLU pipeline, a critical addition for EVAs to en- sure accuracy. Our focus is on creating accurate and scalable SLU that can be deployed rapidly for a large class of EVA applications with little need for human intervention.
Content-based Stance Classification of Tweets about the 2020 Italian Constitutional Referendum

On September 2020 a constitutional referendum was held in Italy. In this work we collect a dataset of 1.2M tweets related to this event, with particular interest to the textual content shared, and we design a hashtag-based semi-automatic approach to label them as Supporters or Against the referendum. We use the labelled dataset to train a classifier based on transformers, unsupervisedly pre-trained on Italian corpora. Our model generalizes well on tweets that cannot be labeled by the hashtag-based approach. We check that no length-, lexicon- and sentiment-biases are present to affect the performance of the classifier. Finally, we discuss the discrepancy between the magnitudes of tweets expressing a specific stance, obtained using both the hashtag-based approach and our trained classifier, and the real outcome of the referendum: the referendum was approved by 70% of the voters, while the number of tweets against the referendum is four times greater than the number of tweets supporting it. We conclude that the Italian referendum was an example of event where the minority was very loud on social media, highly influencing the perception of the event. Analyzing only the activity on social media is dangerous and can lead to extremely wrong forecasts.
Almost Free Semantic Draft for Neural Machine Translation

Translation quality can be improved by global information from the required target sentence because the decoder can understand both past and future information. However, the model needs additional cost to produce and consider such global information. In this work, to inject global information but also save cost, we present an efficient method to sample and consider a semantic draft as global information from semantic space for decoding with almost free of cost. Unlike other successful adaptations, we do not have to perform an EM-like process that repeatedly samples a possible semantic from the semantic space. Empirical experiments show that the presented method can achieve competitive performance in common language pairs with a clear advantage in inference efficiency. We will open all our source code on GitHub.
Choose Your Own Adventure: Paired Suggestions in Collaborative Writing for Evaluating Story Generation Models

Story generation is an open-ended and subjective task, which poses a challenge for evaluating story generation models. We present Choose Your Own Adventure, a collaborative writing setup for pairwise model evaluation. Two models generate suggestions to people as they write a short story; we ask writers to choose one of the two suggestions, and we observe which model’s suggestions they prefer. The setup also allows further analysis based on the revisions people make to the suggestions. We show that these measures, combined with automatic metrics, provide an informative picture of the models’ performance, both in cases where the differences in generation methods are small (nucleus vs. top-k sampling) and large (GPT2 vs. Fusion models).
TITA: A Two-stage Interaction and Topic-Aware Text Matching Model

In this paper, we focus on the problem of keyword and document matching by considering different relevance levels. In our recommendation system, different people follow different hot keywords with interest. We need to attach documents to each keyword and then distribute the documents to people who follow these keywords. The ideal documents should have the same topic with the keyword, which we call topic-aware relevance. In other words, topic-aware relevance documents are better than partially-relevance ones in this application. However, previous tasks never define topic-aware relevance clearly. To tackle this problem, we define a three-level relevance in keyword-document matching task: topic-aware relevance, partially-relevance and irrelevance. To capture the relevance between the short keyword and the document at above-mentioned three levels, we should not only combine the latent topic of the document with its deep neural representation, but also model complex interactions between the keyword and the document. To this end, we propose a Two-stage Interaction and Topic-Aware text matching model (TITA). In terms of “topic-aware”, we introduce neural topic model to analyze the topic of the document and then use it to further encode the document. In terms of “two-stage interaction”, we propose two successive stages to model complex interactions between the keyword and the document. Extensive experiments reveal that TITA outperforms other well-designed baselines and shows excellent performance in our recommendation system.
Constrained Multi-Task Learning for Event Coreference Resolution

We propose a neural event coreference model in which event coreference is jointly trained with five tasks: trigger detection, entity coreference, anaphoricity determination, realis detection, and argument extraction. To guide the learning of this complex model, we incorporate cross-task consistency constraints into the learning process as soft constraints via designing penalty functions. In addition, we propose the novel idea of viewing entity coreference and event coreference as a single coreference task, which we believe is a step towards a unified model of coreference resolution. The resulting model achieves state-of-the-art results on the KBP 2017 event coreference dataset.
Extending Multi-Document Summarization Evaluation to the Interactive Setting

Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization have been proposed in previous work but these solutions are highly divergent and incomparable. In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session. Our framework includes a procedure of collecting real user sessions, as well as evaluation measures relying on summarization standards, but adapted to reflect interaction. All of our solutions and resources are available publicly as a benchmark, allowing comparison of future developments in interactive summarization, and spurring progress in its methodological evaluation. We demonstrate the use of our framework by evaluating and comparing baseline implementations that we developed for this purpose, which will serve as part of our benchmark. Our extensive experimentation and analysis motivate the proposed evaluation framework design and support its viability.
Using Psychologically-Informed Priors for Suicide Prediction in the CLPsych 2021 Shared Task

This paper describes our approach to the CLPsych 2021 Shared Task, in which we aimed to predict suicide attempts based on Twitter feed data. We addressed this challenge by emphasizing reliance on prior domain knowledge. We engineered novel theory-driven features, and integrated prior knowledge with empirical evidence in a principled manner using Bayesian modeling. While this theory-guided approach increases bias and lowers accuracy on the training set, it was successful in preventing over-fitting. The models provided reasonable classification accuracy on unseen test data (0.68<=AUC<= 0.84). Our approach may be particularly useful in prediction tasks trained on a relatively small data set.
Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection

The state-of-the-art abusive language detection models report great in-corpus performance, but underperform when evaluated on abusive comments that differ from the training scenario. As human annotation involves substantial time and effort, models that can adapt to newly collected comments can prove to be useful. In this paper, we investigate the effectiveness of several Unsupervised Domain Adaptation (UDA) approaches for the task of cross-corpora abusive language detection. In comparison, we adapt a variant of the BERT model, trained on large-scale abusive comments, using Masked Language Model (MLM) fine-tuning. Our evaluation shows that the UDA approaches result in sub-optimal performance, while the MLM fine-tuning does better in the cross-corpora setting. Detailed analysis reveals the limitations of the UDA approaches and emphasizes the need to build efficient adaptation methods for this task.
Using Confidential Data for Domain Adaptation of Neural Machine Translation

We study the problem of domain adaptation in Neural Machine Translation (NMT) when domain-specific data cannot be shared due to confidentiality or copyright issues. As a first step, we propose to fragment data into phrase pairs and use a random sample to fine-tune a generic NMT model instead of the full sentences. Despite the loss of long segments for the sake of confidentiality protection, we find that NMT quality can considerably benefit from this adaptation, and that further gains can be obtained with a simple tagging technique.
Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources

While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia. In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. Our model implicitly learns a high-quality mapping for different formalisms across diverse languages without resorting to word alignment and/or translation techniques. We find that, not only is our cross-lingual system competitive with the current state of the art but that it is also robust to low-data scenarios. Most interestingly, our unified model is able to annotate a sentence in a single forward pass with all the inventories it was trained with, providing a tool for the analysis and comparison of linguistic theories across different languages. We release our code and model at https://github.com/SapienzaNLP/unify-srl.
Much Gracias: Semi-supervised Code-switch Detection for Spanish-English: How far can we get?

Because of globalization, it is becoming more and more common to use multiple languages in a single utterance, also called code-switching. This results in special linguistic structures and, therefore, poses many challenges for Natural Language Processing. Existing models for language identification in code-switched data are all supervised, requiring annotated training data which is only available for a limited number of language pairs. In this paper, we explore semi-supervised approaches, that exploit out-of-domain mono-lingual training data. We experiment with word uni-grams, word n-grams, character n-grams, Viterbi Decoding, Latent Dirichlet Allocation, Support Vector Machine and Logistic Regression. The Viterbi model was the best semi-supervised model, scoring a weighted F1 score of 92.23%, whereas a fully supervised state-of-the-art BERT-based model scored 98.43%.
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks

This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks. Although some CL techniques have been proposed for document sentiment classification, we are not aware of any CL work on ASC. A CL system that incrementally learns a sequence of ASC tasks should address the following two issues: (1) transfer knowledge learned from previous tasks to the new task to help it learn a better model, and (2) maintain the performance of the models for previous tasks so that they are not forgotten. This paper proposes a novel capsule network based model called B-CL to address these issues. B-CL markedly improves the ASC performance on both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of B-CL is demonstrated through extensive experiments.