Miracle-dz/UniFAR-FTUs huggingface dataset:
How Positive Are You: Text Style Transfer using Adaptive Style Embedding

The prevalent approach for unsupervised text style transfer is disentanglement between content and style. However, it is difficult to completely separate style information from the content. Other approaches allow the latent text representation to contain style and the target style to affect the generated output more than the latent representation does. In both approaches, however, it is impossible to adjust the strength of the style in the generated output. Moreover, those previous approaches typically perform both the sentence reconstruction and style control tasks in a single model, which complicates the overall architecture. In this paper, we address these issues by separating the model into a sentence reconstruction module and a style module. We use the Transformer-based autoencoder model for sentence reconstruction and the adaptive style embedding is learned directly in the style module. Because of this separation, each module can better focus on its own task. Moreover, we can vary the style strength of the generated sentence by changing the style of the embedding expression. Therefore, our approach not only controls the strength of the style, but also simplifies the model architecture. Experimental results show that our approach achieves better style transfer performance and content preservation than previous approaches.
Understanding Translationese in Multi-view Embedding Spaces

Recent studies use a combination of lexical and syntactic features to show that footprints of the source language remain visible in translations, to the extent that it is possible to predict the original source language from the translation. In this paper, we focus on embedding-based semantic spaces, exploiting departures from isomorphism between spaces built from original target language and translations into this target language to predict relations between languages in an unsupervised way. We use different views of the data — words, parts of speech, semantic tags and synsets — to track translationese. Our analysis shows that (i) semantic distances between original target language and translations into this target language can be detected using the notion of isomorphism, (ii) language family ties with characteristics similar to linguistically motivated phylogenetic trees can be inferred from the distances and (iii) with delexicalised embeddings exhibiting source-language interference most significantly, other levels of abstraction display the same tendency, indicating the lexicalised results to be not “just” due to possible topic differences between original and translated texts. To the best of our knowledge, this is the first time departures from isomorphism between embedding spaces are used to track translationese.
HierCC: a multi-level clustering scheme for population assignments based on core genome MLST

Motivation Routine infectious disease surveillance is increasingly based on large-scale whole genome sequencing databases. Real-time surveillance would benefit from immediate assignments of each genome assembly to hierarchical population structures. Here we present HierCC, a scalable clustering scheme based on core genome multi-locus typing that allows incremental, static, multi-level cluster assignments of genomes. We also present HCCeval, which identifies optimal thresholds for assigning genomes to cohesive HierCC clusters. HierCC was implemented in EnteroBase in 2018, and has since genotyped >400,000 genomes from Salmonella, Escherichia, Yersinia and Clostridioides. Availability Implementation: http://enterobase.warwick.ac.uk/ and Source codes: https://github.com/zheminzhou/HierCC Contact zhemin.zhou@warwick.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.
Systematics of Calliophis intestinalis with the Resurrection of Calliophis nigrotaeniatus (Elapidae, Serpentes)

The red-bellied form of Calliophis intestinalis (Laurenti, 1768) sensu lato was originally reported from Pahang, west Malaysia. To determine the taxonomic status of this form, we examined the type specimens of Elaps sumatranus Lidth De Jeude, 1890, Calliophis intestinalis everetti (Boulenger, 1896), and Callophis furcatus var. nigrotaeniatus Peters, 1863. The results indicated that the red-bellied form of C. intestinalis should be named as Calliophis nigrotaeniatus comb. nov., whose valid species status was based on morphological and molecular analyses. We designate a lectotype and redescribe the species, which is genetically close to Calliophis bilineatus (Peters, 1881) from the Philippines, and is clearly distinguishable from other congeners by possessing a pair of gray or dark blue lateral stripes and by being bright red on the ventrum. Elaps sumatranus and C. i. everetti are relegated to subjective junior synonyms of C. nigrotaeniatus.
Improving Relation Extraction with Relational Paraphrase Sentences

Supervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit the performance of RE. To increase the coverage of relation expressions, we may enlarge the labeled data by hiring annotators or applying Distant Supervision (DS). However, the human-annotated data is costly and non-scalable while the distantly supervised data contains many noises. In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences. Based on an existing labeled data, we first automatically build a task-specific paraphrase data. Then, we propose a novel model to learn the information of diverse relation expressions. In our model, we try to capture this information on the paraphrases via a joint learning framework. Finally, we conduct experiments on a widely used dataset and the experimental results show that our approach is effective to improve the performance on relation extraction, even compared with a strong baseline.
Robust Stability Analysis of Delayed Stochastic Neural Networks via Wirtinger-Based Integral Inequality

We discuss stability analysis for uncertain stochastic neural networks (SNNs) with time delay in this letter. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and utilizing Wirtinger inequalities for estimating the integral inequalities, the delay-dependent stochastic stability conditions are derived in terms of linear matrix inequalities (LMIs). We discuss the parameter uncertainties in terms of norm-bounded conditions in the given interval with constant delay. The derived conditions ensure that the global, asymptotic stability of the states for the proposed SNNs. We verify the effectiveness and applicability of the proposed criteria with numerical examples.
Enhanced Signal Detection by Adaptive Decorrelation of Interspike Intervals

Spike trains with negative interspike interval (ISI) correlations, in which long/short ISIs are more likely followed by short/long ISIs, are common in many neurons. They can be described by stochastic models with a spike-triggered adaptation variable. We analyze a phenomenon in these models where such statistically dependent ISI sequences arise in tandem with quasi-statistically independent and identically distributed (quasi-IID) adaptation variable sequences. The sequences of adaptation states and resulting ISIs are linked by a nonlinear decorrelating transformation. We establish general conditions on a family of stochastic spiking models that guarantee this quasi-IID property and establish bounds on the resulting baseline ISI correlations. Inputs that elicit weak firing rate changes in samples with many spikes are known to be more detectible when negative ISI correlations are present because they reduce spike count variance; this defines a variance-reduced firing rate coding benchmark. We performed a Fisher information analysis on these adapting models exhibiting ISI correlations to show that a spike pattern code based on the quasi-IID property achieves the upper bound of detection performance, surpassing rate codes with the same mean rate—including the variance-reduced rate code benchmark—by 20% to 30%. The information loss in rate codes arises because the benefits of reduced spike count variance cannot compensate for the lower firing rate gain due to adaptation. Since adaptation states have similar dynamics to synaptic responses, the quasi-IID decorrelation transformation of the spike train is plausibly implemented by downstream neurons through matched postsynaptic kinetics. This provides an explanation for observed coding performance in sensory systems that cannot be accounted for by rate coding, for example, at the detection threshold where rate changes can be insignificant.
De novo activated transcription of inserted foreign coding sequences is inheritable in the plant genome

The manner in which inserted foreign coding sequences become transcriptionally activated and fixed in the plant genome is poorly understood. To examine such processes of gene evolution, we performed an artificial evolutionary experiment in Arabidopsis thaliana. As a model of gene-birth events, we introduced a promoterless coding sequence of the firefly luciferase (LUC) gene and established 386 T2-generation transgenic lines. Among them, we determined the individual LUC insertion loci in 76 lines and found that one-third of them were transcribed de novo even in the intergenic or inherently unexpressed regions. In the transcribed lines, transcription-related chromatin marks were detected across the newly activated transcribed regions. These results agreed with our previous findings in A. thaliana cultured cells under a similar experimental scheme. A comparison of the results of the T2-plant and cultured cell experiments revealed that the de novo-activated transcription concomitant with local chromatin remodelling was inheritable. During one-generation inheritance, it seems likely that the transcription activities of the LUC inserts trapped by the endogenous genes/transcripts became stronger, while those of de novo transcription in the intergenic/untranscribed regions became weaker. These findings may offer a clue for the elucidation of the mechanism by which inserted foreign coding sequences become transcriptionally activated and fixed in the plant genome.
QSM Reconstruction Challenge 2.0: Design and Report of Results

Purpose The aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data. Methods A two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground-truths available to mimic the clinical setting. At the second stage, ground-truths were provided for parameter optimization.Submissions were evaluated using eight numerical metrics and visual ratings. Results A total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep-learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and Total Variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art. Conclusion The synthetic data provides a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithmsproduced the best results along all metrics. Future QSM challenges should asses if this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation

Recent high scores on pronoun translation using context-aware neural machine translation have suggested that current approaches work well. ContraPro is a notable example of a contrastive challenge set for English→German pronoun translation. The high scores achieved by transformer models may suggest that they are able to effectively model the complicated set of inferences required to carry out pronoun translation. This entails the ability to determine which entities could be referred to, identify which entity a source-language pronoun refers to (if any), and access the target-language grammatical gender for that entity. We first show through a series of targeted adversarial attacks that in fact current approaches are not able to model all of this information well. Inserting small amounts of distracting information is enough to strongly reduce scores, which should not be the case. We then create a new template test set ContraCAT, designed to individually assess the ability to handle the specific steps necessary for successful pronoun translation. Our analyses show that current approaches to context-aware NMT rely on a set of surface heuristics, which break down when translations require real reasoning. We also propose an approach for augmenting the training data, with some improvements.
How Relevant Are Selectional Preferences for Transformer-based Language Models?

Selectional preference is defined as the tendency of a predicate to favor particular arguments within a certain linguistic context, and likewise, reject others that result in conflicting or implausible meanings. The stellar success of contextual word embedding models such as BERT in NLP tasks has led many to question whether these models have learned linguistic information, but up till now, most research has focused on syntactic information. We investigate whether Bert contains information on the selectional preferences of words, by examining the probability it assigns to the dependent word given the presence of a head word in a sentence. We are using word pairs of head-dependent words in five different syntactic relations from the SP-10K corpus of selectional preference (Zhang et al., 2019b), in sentences from the ukWaC corpus, and we are calculating the correlation of the plausibility score (from SP-10K) and the model probabilities. Our results show that overall, there is no strong positive or negative correlation in any syntactic relation, but we do find that certain head words have a strong correlation and that masking all words but the head word yields the most positive correlations in most scenarios –which indicates that the semantics of the predicate is indeed an integral and influential factor for the selection of the argument.
Centromere drive and suppression by parallel pathways for recruiting microtubule destabilizers

Selfish centromere DNA sequences bias their transmission to the egg in female meiosis. Evolutionary theory suggests that centromere proteins evolve to suppress costs of this “centromere drive”. In hybrid mouse models with genetically different maternal and paternal centromeres, selfish centromere DNA exploits a kinetochore pathway to recruit microtubule-destabilizing proteins that act as drive effectors. We show that such functional differences are suppressed by a parallel pathway for effector recruitment by heterochromatin, which is similar between centromeres in this system. Disrupting heterochromatin by CENP-B deletion amplifies functional differences between centromeres, whereas disrupting the kinetochore pathway with a divergent allele of CENP-C reduces the differences. Molecular evolution analyses using newly sequenced Murinae genomes identify adaptive evolution in proteins in both pathways. We propose that centromere proteins have recurrently evolved to minimize the kinetochore pathway, which is exploited by selfish DNA, relative to the heterochromatin pathway that equalizes centromeres, while maintaining essential functions.
Influence of Aging on the Retina and Visual Motion Processing for Optokinetic Responses in Mice

The decline in visual function due to normal aging impacts various aspects of our daily lives. Previous reports suggest that the aging retina exhibits mislocalization of photoreceptor terminals and reduced amplitudes of scotopic and photopic electroretinogram (ERG) responses in mice. These abnormalities are thought to contribute to age-related visual impairment; however, the extent to which visual function is impaired by aging at the organismal level is unclear. In the present study, we focus on the age-related changes of the optokinetic responses (OKRs) in visual processing. Moreover, we investigated the initial and late phases of the OKRs in young adult (2–3 months old) and aging mice (21–24 months old). The initial phase was evaluated by measuring the open-loop eye velocity of OKRs using sinusoidal grating patterns of various spatial frequencies (SFs) and moving at various temporal frequencies (TFs) for 0.5 s. The aging mice exhibited initial OKRs with a spatiotemporal frequency tuning that was slightly different from those in young adult mice. The late-phase OKRs were investigated by measuring the slow-phase velocity of the optokinetic nystagmus evoked by sinusoidal gratings of various spatiotemporal frequencies moving for 30 s. We found that optimal SF and TF in the normal aging mice are both reduced compared with those in young adult mice. In addition, we measured the OKRs of 4.1G-null (4.1G–/–) mice, in which mislocalization of photoreceptor terminals is observed even at the young adult stage. We found that the late phase OKR was significantly impaired in 4.1G–/– mice, which exhibit significantly reduced SF and TF compared with control mice. These OKR abnormalities observed in 4.1G–/– mice resemble the abnormalities found in normal aging mice. This finding suggests that these mice can be useful mouse models for studying the aging of the retinal tissue and declining visual function. Taken together, the current study demonstrates that normal aging deteriorates to visual motion processing for both the initial and late phases of OKRs. Moreover, it implies that the abnormalities of the visual function in the normal aging mice are at least partly due to mislocalization of photoreceptor synapses.
Differential Induction of the ADAM17 Regulators iRhom1 and 2 in Endothelial Cells

Background: Endothelial function significantly depends on the proteolytic release of surface expressed signal molecules, their receptors and adhesion molecules via the metalloproteinase ADAM17. The pseudoproteases iRhom1 and 2 independently function as adapter proteins for ADAM17 and are essential for the maturation, trafficking, and activity regulation of ADAM17. Bioinformatic data confirmed that immune cells predominantly express iRhom2 while endothelial cells preferentially express iRhom1. Objective: Here, we investigate possible reasons for higher iRhom1 expression and potential inflammatory regulation of iRhom2 in endothelial cells and analyze the consequences for ADAM17 maturation and function. Methods: Primary endothelial cells were cultured in absence and presence of flow with and without inflammatory cytokines (TNFα and INFγ). Regulation of iRhoms was studied by qPCR, involved signaling pathways were studied with transcriptional inhibitors and consequences were analyzed by assessment of ADAM17 maturation, surface expression and cleavage of the ADAM17 substrate junctional adhesion molecule JAM-A. Results: Endothelial iRhom1 is profoundly upregulated by physiological shear stress. This is accompanied by a homeostatic phenotype driven by the transcription factor KLF2 which is, however, only partially responsible for regulation of iRhom1. By contrast, iRhom2 is most prominently upregulated by inflammatory cytokines. This correlates with an inflammatory phenotype driven by the transcription factors NFκB and AP-1 of which AP-1 is most relevant for iRhom2 regulation. Finally, shear stress exposure and inflammatory stimulation have independent and no synergistic effects on ADAM17 maturation, surface expression and JAM-A shedding. Conclusion: Conditions of shear stress and inflammation differentially upregulate iRhom1 and 2 in primary endothelial cells which then results in independent regulation of ADAM17.
Multiple Exposures Enhance Both Item Memory and Contextual Memory Over Time

Repetition learning is an efficient way to enhance memory performance in our daily lives and educational practice. However, it is unclear to what extent repetition or multiple exposures modulate different types of memory over time. The inconsistent findings on it may be associated with encoding strategy. In this study, participants were presented with pairs of pictures (same, similar, and different) once (see section “Experiment 1”) or three times (see section “Experiment 2”) and were asked to make a same/similar/different judgment. By this, an elaborative encoding is more required for the “same” and “similar” conditions than the “different” condition. Then after intervals of 10 min, 1 day, and 1 week, they were asked to perform a recognition test to discriminate a repeated and a similar picture, followed by a remember/know/guess assessment and a contextual judgment. The results showed that after learning the objects three times, both item memory and contextual memory improved. Multiple exposures enhanced the hit rate for the “same” and “similar” conditions, but did not change the false alarm rate significantly. The recollection, rather than the familiarity, contributed to the repetition effect. In addition, the memory enhancement was manifested in each encoding condition and retention interval, especially for the “same” condition and at 10-min and 1-day intervals. These results clarify how repetition influences item and contextual memories during discriminative learning and suggest that multiple exposures render the details more vividly remembered and retained over time when elaborative encoding is emphasized.
Antigen Specificity Enhances Disease Control by Tregs in Vitiligo

Vitiligo is an autoimmune skin disease characterized by melanocyte destruction. Regulatory T cells (Tregs) are greatly reduced in vitiligo skin, and replenishing peripheral skin Tregs can provide protection against depigmentation. Ganglioside D3 (GD3) is overexpressed by perilesional epidermal cells, including melanocytes, which prompted us to generate GD3-reactive chimeric antigen receptor (CAR) Tregs to treat vitiligo. Mice received either untransduced Tregs or GD3-specific Tregs to test the hypothesis that antigen specificity contributes to reduced autoimmune reactivity in vitro and in vivo. CAR Tregs displayed increased IL-10 secretion in response to antigen, provided superior control of cytotoxicity towards melanocytes, and supported a significant delay in depigmentation compared to untransduced Tregs and vehicle control recipients in a TCR transgenic mouse model of spontaneous vitiligo. The latter findings were associated with a greater abundance of Tregs and melanocytes in treated mice versus both control groups. Our data support the concept that antigen-specific Tregs can be prepared, used, and stored for long-term control of progressive depigmentation.