Coherence of a Text and its Topology

Saloni, Zygmunt; Trybulec, Andrzej

doi:10.1515/semi.1974.11.2.101

Cited by 8 publications

(6 citation statements)

References 1 publication

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“…Since diverse studies have found some of the exact same mechanisms äs well äs the same type of construct to be essential for cohesion, that type construct will be used äs a basis for the present empirical approach. The criterial quality of this type of construct is made more apparent by global models of text and discourse, which are not directly concerned with unit connecting links (Schlesinger 1974;Trybulec and Saloni 1974) that explicitly carve out a place for such a link construct within their models.…”

Section: Cohesion and Discoursementioning

confidence: 99%

“…The following conceptual formats qualify äs members of the unit relationship category: ellipsis (Lipski 1974;Gunter 1974;Halliday and Hasan 1975;Speier 1972), conjunction (Halliday and Hasan 1975;DuBois 1973), presupposition (Lipski 1974;VanDtfk 1973), repetition (Huisman 1973;Halliday and Hasan 1975;Bellert 1970;Trybulec and Saloni 1974), pronominalization (Crowell 1973;Van Dy'k 1973), and anaphoric reference (CroweU 1973;Halliday and Hasan 1975;Van Dijk 1973). This list is not exhaustive and is only representative of the various unit relationships so far isolated and the contributing researchers.…”

Section: Cohesion and Discoursementioning

confidence: 99%

“…Each unit relationship provides a qualitatively distinctive basis from which a quanta of cohesion can be produced. More elaborate treatments of cohesion can be found elsewhere (Trybulec and Saloni 1974;Halliday and Hasan 1975;Bellert 1970). 2 This definition is consistent with such treatments which focus on the dependence of one fragment on another.…”

Section: Cohesion and Discoursementioning

confidence: 99%

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Elements of Cohesion Between Turns in Ordinary Conversation

Vuchinich¹

1977

Semiotica

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Section: Cohesion and Discoursementioning

confidence: 99%

Section: Cohesion and Discoursementioning

confidence: 99%

Section: Cohesion and Discoursementioning

confidence: 99%

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Elements of Cohesion Between Turns in Ordinary Conversation

Vuchinich¹

1977

Semiotica

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“…Notably, the human voice carries a wealth of information, and subtle changes or irregularities in speech patterns can sometimes be indicative of underlying health conditions [19,20]. Specifically, voice analysis has been used for emotion recognition in order to improve the human-machine interaction and to monitor the affective state of individuals [21][22][23], for the discrimination of younger and older adults [24], as well as for the early diagnosis of vocal apparatus diseases [25][26][27][28]. For instance, Jothilakshmi developed a system based on Mel-frequency cepstral coefficients and linear prediction cepstral coefficients and used a Gaussian mixture model and hidden Markov model classifiers, reaching 94.44% efficiency in classifying normal and pathological voices [29].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Voice Disorders Using Machine Learning and Vocal Analysis of Voice Samples Recorded through Smartphones

Di Cesare,

Perpetuini,

Cardone

et al. 2024

BioMedInformatics

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Background: The integration of edge computing into smart healthcare systems requires the development of computationally efficient models and methodologies for monitoring and detecting patients’ healthcare statuses. In this context, mobile devices, such as smartphones, are increasingly employed for the purpose of aiding diagnosis, treatment, and monitoring. Notably, smartphones are widely pervasive and readily accessible to a significant portion of the population. These devices empower individuals to conveniently record and submit voice samples, thereby potentially facilitating the early detection of vocal irregularities or changes. This research focuses on the creation of diverse machine learning frameworks based on vocal samples captured by smartphones to distinguish between pathological and healthy voices. Methods: The investigation leverages the publicly available VOICED dataset, comprising 58 healthy voice samples and 150 samples from voices exhibiting pathological conditions, and machine learning techniques for the classification of healthy and diseased patients through the employment of Mel-frequency cepstral coefficients. Results: Through cross-validated two-class classification, the fine k-nearest neighbor exhibited the highest performance, achieving an accuracy rate of 98.3% in identifying healthy and pathological voices. Conclusions: This study holds promise for enabling smartphones to effectively identify vocal disorders, offering a multitude of advantages for both individuals and healthcare systems, encompassing heightened accessibility, early detection, and continuous monitoring.

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“…Docking has been applied in proposing the efficacy of statins, with molecular docking aiding in identifying remdesivir and proposing it as a potentially effective drug due to its ability to target the RNA-dependent RNA polymerase (RdRp) [22,23]. Potentially effective small molecules have been prioritized using techniques like molecular docking simulations and network-based repurposing [24,25]. Molecular docking, aided by ML, has been a prevalent approach for virtual screening against the various SARS-CoV-2 proteins essential for viral replication and infection, including 3CLpro, Spike Protein, RdRP, M pro , ACE2 receptor, and TMPRSS2 protease [26,27], with deep learning-aided approaches gaining prominence for their automatic feature extraction, expediting discovery (Figure 2).…”

Section: Introductionmentioning

confidence: 99%

In Silico Therapeutic Study: The Next Frontier in the Fight against SARS-CoV-2 and Its Variants

Wei,

Basharat,

Lang’at

2024

DDC

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COVID-19 has claimed around 7 million lives (from December 2019–November 2023) worldwide and continues to impact global health. SARS-CoV-2, the virus causing COVID-19 disease, is characterized by a high rate of mutations, which contributes to its rapid spread, virulence, and vaccine escape. While several vaccines have been produced to minimize the severity of the coronavirus, and diverse treatment regimens have been approved by the US FDA under Emergency Use Authorization (EUA), SARS-CoV-2 viral mutations continue to derail the efforts of scientists as the emerging variants evade the recommended therapies. Nonetheless, diverse computational models exist that offer an opportunity for the swift development of new drugs or the repurposing of old drugs. In this review, we focus on the use of various virtual screening techniques like homology modeling, molecular docking, molecular dynamics simulations, QSAR, pharmacophore modeling, etc., in repurposing SARS-CoV-2 therapeutics against major variants of SARS-CoV-2 (Alpha, Beta, Gamma, Delta, and Omicron). The results have been promising from the computer-aided drug design (CADD) studies in suggesting potential compounds for the treatment of COVID-19 variants. Hence, in silico therapeutic studies represent a transformative approach that holds great promise in advancing our fight against the ever-evolving landscape of SARS-CoV-2 and its variants.

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Coherence of a Text and its Topology

Cited by 8 publications

References 1 publication

Elements of Cohesion Between Turns in Ordinary Conversation

Elements of Cohesion Between Turns in Ordinary Conversation

Assessment of Voice Disorders Using Machine Learning and Vocal Analysis of Voice Samples Recorded through Smartphones

In Silico Therapeutic Study: The Next Frontier in the Fight against SARS-CoV-2 and Its Variants

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