Keyword Index for Volume 99
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC2607235 | biostudies-literature | 2008 Dec
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Neural keyword spotting could form the basis of a speech brain-computer-interface for menu-navigation if it can be done with low latency and high specificity comparable to the "wake-word" functionality of modern voice-activated AI assistant technologies. This study investigated neural keyword spotting using motor representations of speech via invasively-recorded electrocorticographic signals as a proof-of-concept. Neural matched filters were created from monosyllabic consonant-vowel utterances: one keyword utterance, and 11 similar non-keyword utterances. These filters were used in an analog to the acoustic keyword spotting problem, applied for the first time to neural data. The filter templates were cross-correlated with the neural signal, capturing temporal dynamics of neural activation across cortical sites. Neural vocal activity detection (VAD) was used to identify utterance times and a discriminative classifier was used to determine if these utterances were the keyword or non-keyword speech. Model performance appeared to be highly related to electrode placement and spatial density. Vowel height (/a/ vs /i/) was poorly discriminated in recordings from sensorimotor cortex, but was highly discriminable using neural features from superior temporal gyrus during self-monitoring. The best performing neural keyword detection (5 keyword detections with two false-positives across 60 utterances) and neural VAD (100% sensitivity, ~1 false detection per 10 utterances) came from high-density (2 mm electrode diameter and 5 mm pitch) recordings from ventral sensorimotor cortex, suggesting the spatial fidelity and extent of high-density ECoG arrays may be sufficient for the purpose of speech brain-computer-interfaces.
| S-EPMC6389788 | biostudies-literature
Project description:The hypothalamus is an important neuroendocrine hub for the control of appetite and satiety. In animal studies it has been established that hypothalamic lesioning or stimulation causes alteration to feeding behaviour and consequently body mass, and exposure to high calorie diets induces hypothalamic inflammation. These findings suggest that alterations in hypothalamic structure and function are both a cause and a consequence of changes to food intake. However, there is limited in vivo human data relating the hypothalamus to obesity or eating disorders, in part due to technical problems relating to its small size. Here, we used a novel automated segmentation algorithm to exploratorily investigate the relationship between hypothalamic volume, normalised to intracranial volume, and body mass index (BMI). The analysis was applied across four independent datasets comprising of young adults (total n = 1,351 participants) spanning a range of BMIs (13.3 - 47.8 kg/m2). We compared underweight (including individuals with anorexia nervosa), healthy weight, overweight and obese individuals in a series of complementary analyses. We report that overall hypothalamic volume is significantly larger in overweight and obese groups of young adults. This was also observed for a number of hypothalamic sub-regions. In the largest dataset (the HCP-Young Adult dataset (n = 1111)) there was a significant relationship between hypothalamic volume and BMI. We suggest that our findings of a positive relationship between hypothalamic volume and BMI is potentially consistent with hypothalamic inflammation as seen in animal models in response to high fat diet, although more research is needed to establish a causal relationship. Overall, we present novel, in vivo findings that link elevated BMI to altered hypothalamic structure. This has important implications for study of the neural mechanisms of obesity in humans.
| S-EPMC10509524 | biostudies-literature
Project description:With the ever-increasing abundance of biomedical articles, improving the accuracy of keyword search results becomes crucial for ensuring reproducible research. However, keyword extraction for biomedical articles is hard due to the existence of obscure keywords and the lack of a comprehensive benchmark. PubMedAKE is an author-assigned keyword extraction dataset that contains the title, abstract, and keywords of over 843,269 articles from the PubMed open access subset database. This dataset, publicly available on Zenodo, is the largest keyword extraction benchmark with sufficient samples to train neural networks. Experimental results using state-of-the-art baseline methods illustrate the need for developing automatic keyword extraction methods for biomedical literature.
| S-EPMC9652778 | biostudies-literature
Project description:BackgroundAtrial volume index and atrial volume have recently been identified as predictors of atrial fibrillation (AF) recurrence following electrical cardioversion or radiofrequency ablation. However, most studies have reported the relationship between LAVI/LAV and AF recurrence, whereas there is little information on the relationship between RAVI/RAV and AF recurrence. Therefore, we performed a meta-analysis to assess the relationship between the risk of AF recurrence and RAVI/RAV in patients with AF who underwent electrical cardioversion or radiofrequency ablation.MethodsCNKI, Wanfang Database, Pubmed, Embase, Cochrane Library, and Web of Science were searched up to October 01, 2024. A meta-analysis of relative risk data from prospective and retrospective cohort studies that reported on the relationship between the risk of AF recurrence and RAVI/RAV in patients with AF after electrical cardioversion or radiofrequency ablation was performed.ResultsThe results showed that patients with AF recurrence had a higher mean right atrial volume index (RAVI) compared to patients with no recurrence. After electrical cardioversion or radiofrequency ablation, RAVI can independently predict the recurrence of AF (OR = 1.06, 95%CI (1.02, 1.11)). The average right atrial volume (RAV) of patients with AF recurrence was higher than that of patients without AF recurrence. After electrical cardioversion or radiofrequency ablation, RAV can independently predict the recurrence of AF (OR = 1.02, 95%CI (1.00, 1.05)).ConclusionPatients with AF recurrence after electrical cardioversion or radio frequency ablation had higher mean RAVI and RAV compared to patients with no recurrence. After electrical cardioversion or radiofrequency ablation in patients with AF, higher levels of RAVI and RAV increase the chance of recurrence of AF.
| S-EPMC11649108 | biostudies-literature
Project description:Scientific production has increased exponentially in recent years. It is necessary to find methodological strategies for understanding holistic or macro views of the major research trends developed in specific fields. Data mining is a useful technique to address this task. In particular, our study presents a global analysis of the information generated during last decades in the Sport Sciences Category (SSC) included in the Web of Science database. An analysis of the frequency of appearance and the dynamics of the Author Keywords (AKs) has been made for the last thirty years. Likewise, the network of co-occurrences established between words and the survival time of new words that have appeared since 2001 has also been analysed. One of the main findings of our research is the identification of six large thematic clusters in the SSC. There are also two major terms that coexist ('REHABILITATION' and 'EXERCISE') and show a high frequency of appearance, as well as a key behaviour in the calculated co-occurrence networks. Another significant finding is that AKs are mostly accepted in the SSC since there has been high percentage of new terms during 2001-2006, although they have a low survival period. These results support a multidisciplinary perspective within the Sport Sciences field of study and a colonization of the field by rehabilitation according to our AK analysis.
| S-EPMC6070281 | biostudies-literature
Project description:We studied microRNA expression levels from serum of postmenopausal women with osteoporosis by investigating the anti-osteoporotic treatment denosumab for a period of 2 years in a longitudinal study. Serum RNA was extracted and subject to small RNA sequencing on an Illumina HiSeqV4 SR50 using 50bp, single end reads. After mapping against GRCh38.p12 provided by Ensembl and miRBase v22.1 differential expression analysis was undertaken with edgeRv3.28 using a quasi-likelihood negative binomial generalized log-linear model. We identified a panel of altered small non-coding RNAs between 3 different time points for all patients.
2023-12-31 | GSE206540 | GEO
Project description:Autophagy functions as a main route for the degradation of superfluous and damaged constituents of the cytoplasm. Defects in autophagy are implicated in the development of various age-dependent degenerative disorders such as cancer, neurodegeneration and tissue atrophy, and in accelerated aging. To promote basal levels of the process in pathological settings, we previously screened a small molecule library for novel autophagy-enhancing factors that inhibit the myotubularin-related phosphatase MTMR14/Jumpy, a negative regulator of autophagic membrane formation. Here we identify AUTEN-99 (autophagy enhancer-99), which activates autophagy in cell cultures and animal models. AUTEN-99 appears to effectively penetrate through the blood-brain barrier, and impedes the progression of neurodegenerative symptoms in Drosophila models of Parkinson's and Huntington's diseases. Furthermore, the molecule increases the survival of isolated neurons under normal and oxidative stress-induced conditions. Thus, AUTEN-99 serves as a potent neuroprotective drug candidate for preventing and treating diverse neurodegenerative pathologies, and may promote healthy aging.
| S-EPMC5311965 | biostudies-literature
Project description:A document's keywords provide high-level descriptions of the content that summarize the document's central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document's content that can reflect the authors' main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors' provided keywords. The average similarity of our approach with author-assigned keywords is 51%.
| S-EPMC9202614 | biostudies-literature