Linguistic feedback supports rapid adaptation to acoustically degraded speech.

Humans can quickly adapt to recognize acoustically degraded speech, and here we hypothesize that the quick adaptation is enabled by internal linguistic feedback - Listeners use partially recognized sentences to adapt the mapping between acoustic features and phonetic labels. We test this hypothesis by quantifying how quickly humans adapt to degraded speech and analyzing whether the adaptation process can be simulated by adapting an automatic speech recognition (ASR) system based on its own speech recognition results. We consider three types of acoustic degradation, i.e., noise vocoding, time compression, and local time-reversal. The human speech recognition rate can increase by >20% after exposure to just a few acoustically degraded sentences. Critically, the ASR system with internal linguistic feedback can adapt to degraded speech with human-level speed and accuracy. These results suggest that self-supervised learning based on linguistic feedback is a plausible strategy for human adaptation to acoustically degraded speech.

Intrasegmental recombination as an evolutionary force of Lassa fever virus.

Lassa fever (LF), caused by Lassa virus (LASV), is one of the most dangerous diseases to public health. Homologous recombination (HR) is a basic genetic power driving biological evolution. However, as a negative-stranded RNA virus, it is unknown whether HR occurs between LASVs and its influence on the outbreak of LF. In this study, after analyzing 575 S and 433 L segments of LASV collected in Africa, we found that LASV can achieve HR in both of its segments. Interestingly, although the length of S segment is less than half of the L segment, the proportion of LASVs with S recombinants is significantly higher than that with L recombinants. These results suggest that HR may be a feature of LASV, which can be set by natural selection to produce beneficial or eliminate harmful mutations for the virus, so it plays a role in LASV evolution during the outbreak of LF.

Original speech and its echo are segregated and separately processed in the human brain.

Speech recognition crucially relies on slow temporal modulations (<16 Hz) in speech. Recent studies, however, have demonstrated that the long-delay echoes, which are common during online conferencing, can eliminate crucial temporal modulations in speech but do not affect speech intelligibility. Here, we investigated the underlying neural mechanisms. MEG experiments demonstrated that cortical activity can effectively track the temporal modulations eliminated by an echo, which cannot be fully explained by basic neural adaptation mechanisms. Furthermore, cortical responses to echoic speech can be better explained by a model that segregates speech from its echo than by a model that encodes echoic speech as a whole. The speech segregation effect was observed even when attention was diverted but would disappear when segregation cues, i.e., speech fine structure, were removed. These results strongly suggested that, through mechanisms such as stream segregation, the auditory system can build an echo-insensitive representation of speech envelope, which can support reliable speech recognition.

Learning Representations from Medical Text for Effective Diagnoses and Knowledge Discovery.

Discovering knowledge and effectively predicting target events are two main goals of medical text mining. However, few models can achieve them simultaneously. In this study, we investigated the possibility of discovering knowledge and predicting diagnosis at once via raw medical text. We proposed the Enhanced Neural Topic Model (ENTM), a variant of the neural topic model, to learn interpretable representations. We introduced the auxiliary loss set to improve the effectiveness of learned representations. Then, we used learned representations to train a softmax regression model to predict target events. As each element in representations learned by the ENTM has an explicit semantic meaning, weights in softmax regression represent potential knowledge of whether an element is a significant factor in predicting diagnosis. We adopted two independent medical text datasets to evaluate our ENTM model. Results indicate that our model performed better than the latest pretrained neural language models. Meanwhile, analysis of model parameters indicates that our model has the potential discover knowledge from data.Clinical relevance- This work provides a model that can effectively predict patient diagnosis and has the potential to discover knowledge from medical text.

Human attention during goal-directed reading comprehension relies on task optimization.

The computational principles underlying attention allocation in complex goal-directed tasks remain elusive. Goal-directed reading, that is, reading a passage to answer a question in mind, is a common real-world task that strongly engages attention. Here, we investigate what computational models can explain attention distribution in this complex task. We show that the reading time on each word is predicted by the attention weights in transformer-based deep neural networks (DNNs) optimized to perform the same reading task. Eye tracking further reveals that readers separately attend to basic text features and question-relevant information during first-pass reading and rereading, respectively. Similarly, text features and question relevance separately modulate attention weights in shallow and deep DNN layers. Furthermore, when readers scan a passage without a question in mind, their reading time is predicted by DNNs optimized for a word prediction task. Therefore, we offer a computational account of how task optimization modulates attention distribution during real-world reading.

Neural substrate underlying the learning of a passage with unfamiliar vocabulary and syntax.

Speech comprehension is a complex process involving multiple stages, such as decoding of phonetic units, recognizing words, and understanding sentences and passages. In this study, we identify cortical networks beyond basic phonetic processing using a novel passage learning paradigm. Participants learn to comprehend a story composed of syllables of their native language, but containing unfamiliar vocabulary and syntax. Three learning methods are employed, each resulting in some degree of learning within a 12-min learning session. Functional magnetic resonance imaging results reveal that, when listening to the same story, the classic temporal-frontal language network is significantly enhanced by learning. Critically, activation of the left anterior and posterior temporal lobe correlates with the learning outcome that is assessed behaviorally through, e.g. word recognition and passage comprehension tests. This study demonstrates that a brief learning session is sufficient to induce neural plasticity in the left temporal lobe, which underlies the transformation from phonetic units to the units of meaning, such as words and sentences.

Phase Property of Envelope-Tracking EEG Response Is Preserved in Patients with Disorders of Consciousness.

When listening to speech, the low-frequency cortical response below 10 Hz can track the speech envelope. Previous studies have demonstrated that the phase lag between speech envelope and cortical response can reflect the mechanism by which the envelope-tracking response is generated. Here, we analyze whether the mechanism to generate the envelope-tracking response is modulated by the level of consciousness, by studying how the stimulus-response phase lag is modulated by the disorder of consciousness (DoC). It is observed that DoC patients in general show less reliable neural tracking of speech. Nevertheless, the stimulus-response phase lag changes linearly with frequency between 3.5 and 8 Hz, for DoC patients who show reliable cortical tracking to speech, regardless of the consciousness state. The mean phase lag is also consistent across these DoC patients. These results suggest that the envelope-tracking response to speech can be generated by an automatic process that is barely modulated by the consciousness state.

Competitive gamification in crowdsourcing-based contextual-aware recommender systems.

During the COVID-19 outbreak, crowdsourcing-based context-aware recommender systems (CARS) which capture the real-time context in a contactless manner played an important role in the "new normal". This study investigates whether this approach effectively supports users' decisions during epidemics and how different game designs affect users performing crowdsourcing tasks. This study developed a crowdsourcing-based CARS focusing on restaurant recommendations. We used four conditions (control, self-competitive, social-competitive, and mixed gamification) and conducted a two-week field study involving 68 users. The system provided recommendations based on real-time contexts including restaurants' epidemic status, allowing users to identify suitable restaurants to visit during COVID-19. The result demonstrates the feasibility of crowdsourcing to collect real-time information for recommendations during COVID-19 and reveals that a mixed competitive game design encourages both high- and low-performance users to engage more and that a game design with self-competitive elements motivates users to take on a wider variety of tasks. These findings inform the design of restaurant recommender systems in an epidemic context and serve as a comparison of incentive mechanisms for gamification of self-competition and competition with others.

An IFNT/FOXO1/PTGS2 axis regulates prostaglandin F2α synthesis in goat uterus during early pregnancy.

In ruminants, IFN-tau (IFNT) regulates the production of prostaglandins (PG) in the endometrium, which is crucial for conceptus adhesion. However, the related molecular regulatory mechanisms remain unclear. Forkhead box O1 (FOXO1), a member of the FOXO subfamily of transcription factors, is known to be important for mouse implantation and decidualization. In this study, we determined the spatiotemporal expression profile of FOXO1 in goat endometrium during early pregnancy. FOXO1 was highly expressed in the glandular epithelium since the onset of conceptus adhesion (d 16 of pregnancy). Then, we validated that FOXO1 could bind to the promoter of prostaglandin-endoperoxide synthase 2 (PTGS2) and increase its transcription. And the expression profile of PTGS2 was similar to that of FOXO1 in the peri-implantation uterus. Moreover, IFNT could upregulate the levels of FOXO1 and PTGS2 in goat uterus and primary endometrial epithelium cells (EEC). In EEC, the intracellular content of PGF2α was positively correlated with the levels of IFNT and FOXO1. Altogether, we found an IFNT/FOXO1/PTGS2 axis that controls the synthesis of PGF2α but not prostaglandin E2 in goat uterine glands. These findings contribute to better understanding the function of FOXO1 in the reproductive physiology of goats and provide more insights into the implantation of small ruminants.

Complex Mapping between Neural Response Frequency and Linguistic Units in Natural Speech.

When listening to connected speech, the human brain can extract multiple levels of linguistic units, such as syllables, words, and sentences. It has been hypothesized that the time scale of cortical activity encoding each linguistic unit is commensurate with the time scale of that linguistic unit in speech. Evidence for the hypothesis originally comes from studies using the frequency-tagging paradigm that presents each linguistic unit at a constant rate, and more recently extends to studies on natural speech. For natural speech, it is sometimes assumed that neural encoding of different levels of linguistic units is captured by the neural response tracking speech envelope in different frequency bands (e.g., around 1 Hz for phrases, around 2 Hz for words, and around 4 Hz for syllables). Here, we analyze the coherence between speech envelope and idealized responses, each of which tracks a single level of linguistic unit. Four units, that is, phones, syllables, words, and sentences, are separately considered. We show that the idealized phone-, syllable-, and word-tracking responses all correlate with the speech envelope both around 3-6 Hz and below ∼1 Hz. Further analyses reveal that the 1-Hz correlation mainly originates from the pauses in connected speech. The results here suggest that a simple frequency-domain decomposition of envelope-tracking activity cannot separate the neural responses to different linguistic units in natural speech.

Speech recognition in echoic environments and the effect of aging and hearing impairment.

Temporal modulations provide critical cues for speech recognition. When the temporal modulations are distorted by, e.g., reverberations, speech intelligibility drops, and the drop in speech intelligibility can be explained by the amount of distortions to the speech modulation spectrum, i.e., the spectrum of temporal modulations. Here, we test a condition in which speech is contaminated by a single echo. Speech is delayed by either 0.125 s or 0.25 s to create an echo, and these two conditions notch out the temporal modulations at 2 or 4 Hz, respectively. We evaluate how well young and older listeners can recognize such echoic speech. For young listeners, the speech recognition rate is not influenced by the echo, even when they are exposed to the first echoic sentence. For older listeners, the speech recognition rate drops to less than 60% when listening to the first echoic sentence, but rapidly recovers to above 75% with exposure to a few sentences. Further analyses reveal that both age and the hearing threshold influence the recognition of echoic speech for the older listeners. These results show that the recognition of echoic speech cannot be fully explained by distortions to the modulation spectrum, and suggest that the auditory system has mechanisms to effectively compensate the influence of single echoes.

METTL3 promotes proliferation of goat endometrial epithelial cells by regulating CTGF in an m6A-dependent manner†.

N6-methyladenosine (m6A), an epigenetic modification on RNAs, plays an important role in many physiological and pathological processes. However, the involvement of m6A in goat uterus during early pregnancy remains largely unknown. In this study, we found that the total m6A level was increasing in goat uterus as early pregnancy progressed. Methyltransferase-like 3 (METTL3) is a core catalytic subunit of the m6A methyltransferase. We thus determined the expression and regulation of METTL3 in goat uterus. METTL3 was highly expressed in the luminal and glandular epithelia from day 16 (D16) to D25 of pregnancy, and it could be up-regulated by estrogen and progesterone in goat uterus and primary endometrial epithelial cells (EECs). In EECs, knockdown or overexpression of METTL3 resulted in a significant decrease or increase of cell proliferation, respectively. METTL3 knockdown reduced the m6A level of not only total RNA but also connective tissue growth factor (CTGF) mRNA. Luciferase assay suggested that METTL3 might target the potential m6A sites in the 3'untranslated region (3'UTR) of CTGF mRNA. Moreover, METTL3 positively regulated CTGF expression, and CTGF knockdown significantly counteracted the promoting effect of METTL3 overexpression on EEC proliferation. Collectively, METTL3 is dynamically expressed in goat uterus and can affect EEC proliferation by regulating CTGF in an m6A-dependent manner. Our results will lay a foundation for further studying the crucial mechanism of METTL3-mediated m6A modification in goat uterus during early pregnancy.

Acoustic correlates of the syllabic rhythm of speech: Modulation spectrum or local features of the temporal envelope.

The syllable is a perceptually salient unit in speech. Since both the syllable and its acoustic correlate, i.e., the speech envelope, have a preferred range of rhythmicity between 4 and 8 Hz, it is hypothesized that theta-band neural oscillations play a major role in extracting syllables based on the envelope. A literature survey, however, reveals inconsistent evidence about the relationship between speech envelope and syllables, and the current study revisits this question by analyzing large speech corpora. It is shown that the center frequency of speech envelope, characterized by the modulation spectrum, reliably correlates with the rate of syllables only when the analysis is pooled over minutes of speech recordings. In contrast, in the time domain, a component of the speech envelope is reliably phase-locked to syllable onsets. Based on a speaker-independent model, the timing of syllable onsets explains about 24% variance of the speech envelope. These results indicate that local features in the speech envelope, instead of the modulation spectrum, are a more reliable acoustic correlate of syllables.

The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI-EEG study.

The amplitude of low-frequency fluctuation (ALFF) describes the regional intensity of spontaneous blood-oxygen-level-dependent signal in resting-state functional magnetic resonance imaging (fMRI). How the fMRI-ALFF relates to the amplitude in electrophysiological signals remains unclear. We here aimed to investigate the neural correlates of fMRI-ALFF by comparing the spatial difference of amplitude between the eyes-closed (EC) and eyes-open (EO) states from fMRI and magnetoencephalography (MEG), respectively. By synthesizing MEG signal into amplitude-based envelope time course, we first investigated 2 types of amplitude in MEG, meaning the amplitude of neural activities from delta to gamma (i.e. MEG-amplitude) and the amplitude of their low-frequency modulation at the fMRI range (i.e. MEG-ALFF). We observed that the MEG-ALFF in EC was increased at parietal sensors, ranging from alpha to beta; whereas the MEG-amplitude in EC was increased at the occipital sensors in alpha. Source-level analysis revealed that the increased MEG-ALFF in the sensorimotor cortex overlapped with the most reliable EC-EO differences observed in fMRI at slow-3 (0.073-0.198 Hz), and these differences were more significant after global mean standardization. Taken together, our results support that (i) the amplitude at 2 timescales in MEG reflect distinct physiological information and that (ii) the fMRI-ALFF may relate to the ALFF in neural activity.

Statistical learning in patients in the minimally conscious state.

When listening to speech, cortical activity can track mentally constructed linguistic units such as words, phrases, and sentences. Recent studies have also shown that the neural responses to mentally constructed linguistic units can predict the outcome of patients with disorders of consciousness (DoC). In healthy individuals, cortical tracking of linguistic units can be driven by both long-term linguistic knowledge and online learning of the transitional probability between syllables. Here, we investigated whether statistical learning could occur in patients in the minimally conscious state (MCS) and patients emerged from the MCS (EMCS) using electroencephalography (EEG). In Experiment 1, we presented to participants an isochronous sequence of syllables, which were composed of either 4 real disyllabic words or 4 reversed disyllabic words. An inter-trial phase coherence analysis revealed that the patient groups showed similar word tracking responses to real and reversed words. In Experiment 2, we presented trisyllabic artificial words that were defined by the transitional probability between words, and a significant word-rate EEG response was observed for MCS patients. These results suggested that statistical learning can occur with a minimal conscious level. The residual statistical learning ability in MCS patients could potentially be harnessed to induce neural plasticity.

Dual interaction between heartbeat-evoked responses and stimuli.

Heartbeat-evoked responses (HERs) can interact with external stimuli and play a crucial role in shaping perception, self-related processes, and emotional processes. On the one hand, the external stimulus could modulate HERs. On the other hand, the HERs could affect cognitive processing of the external stimulus. Whether the same neural mechanism underlies these two processes, however, remains unclear. Here, we investigated this interactive mechanism by measuring HERs using magnetoencephalography (MEG) and two name perception tasks. Specifically, we tested (1) how hearing a subject's own name (SON) modulates HERs and (2) how the judgment of an SON is biased by prestimulus HERs. The results showed a dual interaction between HERs and SON. In particular, SON can modulate HERs for heartbeats occurring from 200 to 1200 ms after SON presentation. In addition, prestimulus HERs can bias the SON judgment when a stimulus is presented. Importantly, MEG activities from these two types of interactions differed in spatial and temporal patterns, suggesting that they may be associated with distinct neural pathways. These findings extend our understanding of brain-heart interactions.

Evolution of cucurbit-infecting tobamoviruses: Recombination and codon usage bias.

Currently, there are seven cucurbit-infecting tobamoviruses comprising cucumber green mottle mosaic virus (CGMMV), Kyuri green mottle mosaic virus (KGMMV), cucumber fruit mottle mosaic virus (CFMMV), zucchini green mottle mosaic virus (ZGMMV), cucumber mottle virus (CMoV), watermelon green mottle mosaic virus (WGMMV), and Trichosanthes mottle mosaic virus (TrMMV). To gain more insights into their evolution, recombination analyses were conducted. Four CGMMV isolates and one KGMMV isolate were suggested to be recombinants. And there was an interspecies recombination event between CGMMV and ZGMMV. Phylogenetic incongruence was also observed for CGMMV and KGMMV. A probable ancestral pattern was inferred for the gene junction region between RdRp and MP. Codon usage bias analysis revealed that the viral genes had additional influence independent of compositional constraint. In codon preference, the seven viruses were both similar to and different from the host cucumber (Cucumis sativus). Moreover, the viruses were not deficient in CpG and UpA dinucleotides.

The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS.

Human language units are hierarchical, and reading acquisition involves integrating multisensory information (typically from auditory and visual modalities) to access meaning. However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in auditory and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory, visual, or combined audio-visual modalities while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e. characters/monosyllabic words) and higher-level linguistic structures (i.e. phrases and sentences) across the 3 modalities separately. We found that audio-visual integration occurs in all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.

Delta-band neural tracking primarily reflects rule-based chunking instead of semantic relatedness between words.

It is debated whether cortical responses matching the time scales of phrases and sentences mediate the mental construction of the syntactic chunks or are simply caused by the semantic properties of words. Here, we investigate to what extent delta-band neural responses to speech can be explained by semantic relatedness between words. To dissociate the contribution of semantic relatedness from sentential structures, participants listened to sentence sequences and paired-word sequences in which semantically related words repeated at 1 Hz. Semantic relatedness in the 2 types of sequences was quantified using a word2vec model that captured the semantic relation between words without considering sentential structure. The word2vec model predicted comparable 1-Hz responses with paired-word sequences and sentence sequences. However, empirical neural activity, recorded using magnetoencephalography, showed a weaker 1-Hz response to paired-word sequences than sentence sequences in a word-level task that did not require sentential processing. Furthermore, when listeners applied a task-related rule to parse paired-word sequences into multi-word chunks, 1-Hz response was stronger than that in word-level task on the same sequences. Our results suggest that cortical activity tracks multi-word chunks constructed by either syntactic rules or task-related rules, whereas the semantic relatedness between words contributes only in a minor way.