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Anti-dengue activity of a cellular lipid uptake inhibitor, lipofermata
End-to-end deep learning for smart maritime threat detection: an AE–CNN–LSTM-based approach
Viral oncogenes drive biphenotypic lymphoproliferative malignancy in transgenic mice
Correction: Effortless 3D radio maps generation for fingerprinting-based indoor positioning system
Experimental optimization of user-habit-oriented daily products via integrated innovation and functional parameter analysis
The impact of injury and illness on team USA performance outcomes at the Paris 2024 summer olympic games
Deep learning for text summarization using NLP for automated news digest
Abstract Text Summarization, a vital aspect of natural language processing, aims to condense text while retaining its essential meaning. This process is achieved through extractive and abstractive methods. Deep Learning faces challenges in this domain, including semantic understanding, preservation of meaning, efficient handling of long documents, and ensuring coherence and grammatical correctness. Despite these challenges, deep learning offers advantages such as time saving, facilitating information retrieval, scalability, and content personalization. Also, deep learning faces the risk of losing important details, subjectivity in information selection, difficulty in handling complex texts, and variability in summary quality. Addressing these challenges remains an ongoing focus of research and development in the field of NLP. This paper proposes text summarization approach utilizing deep learning models, namely T5-base, T5-large, BART CNN, and PEGASUS. The methodology involves initial data cleaning and preprocessing of the dataset, followed by exploratory data analysis (EDA) to gain insights into the data. Subsequently, the Rouge and BLUE scores of each model are calculated to assess their summarization performance. After training the models, the Rouge and BLUE scores are re-evaluated to measure their effectiveness in generating summaries. The primary objective is to compare the performance of these models based on their Rouge scores, aiming to identify the model that provides the highest Rouge score, indicative of better summary quality. This study contributes to the advancement of text summarization techniques and provide insights into the effectiveness of various deep learning models in this domain.
RHOBTB2 enhances cell proliferation of acute myeloid leukemia by modulating Hippo-YAP1 signaling and dependent of KLHL13
Exploratory approach to speculate on body composition models for elite teenage basketball players
Modelling crop growth and soil hydrothermal regimes under conservation agriculture using APSIM-wheat
Surface oxidation of FeSiB amorphous alloy and its effect on metallic glass fiber reinforced cement mortar
Design of tri-band bandpass filter based on the interdigital structure and rectangular resonator for LTE and radar applications
Pleasant odors specifically promote a soothing autonomic response and brain–body coupling through respiratory modulation
Abstract Neuronal oscillations are tightly linked to physiological rhythms, yet little is known about how sensory stimuli influence their coupling. Here, we investigated whether pleasant odors and pleasant music modulate the coupling between respiration, autonomic activity, and brain oscillations. Using a within-subject design, we exposed participants to a personally pleasant odor and a personally pleasant music while recording respiratory and cardiac activity, feeling of relaxation, and respiration-locked EEG responses. Our results reveal that only odors induced a significant coupling between physiological and neural rhythms. Pleasant odors decreased respiratory rate, increased inspiratory volume, reduced heart rate, and enhanced heart rate variability (HRV). EEG analyses showed that this odor-induced respiratory slowing enhanced respiration-related brain activity in a temporo-parieto-central network, which is not observed with music. In contrast, music, regardless of tempo, increased subjective arousal without affecting autonomic or neural coupling. These findings suggest that pleasant odors engage an olfactomotor response, where an emotionally driven change in breathing rhythm enhances respiration-related neural oscillations. This mechanism may facilitate brain states associated with an increased feeling of relaxation and interoception, distinguishing odors as a unique sensory modality capable of promoting brain-body coupling. Our study highlights the potential of olfactory stimulation as a non-invasive tool for modulating brain rhythms and autonomic function.
A probabilistic detection-based approach to skin and freckle segmentation
Association of hyperemesis gravidarum severity with HALP score and hematologic inflammatory markers
Abstract This study aimed to investigate the diagnostic and prognostic value of the HALP score and hematological inflammatory indices in pregnant women with hyperemesis gravidarum (HEG). In this prospective observational study, 48 pregnant women diagnosed with HEG and 51 healthy, gestational age-matched controls were enrolled. Demographic, clinical, and laboratory data were collected. Hematological markers, including the HALP score, systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR), were calculated. The severity of HEG was assessed using the Pregnancy-Unique Quantification of Emesis and Nausea (PUQE) score. Body mass index, platelet count, lymphocyte count, and albumin levels were significantly lower in the HEG group, whereas NLR, MLR, and SII were significantly higher (p < 0.05 for all). The HALP score was also significantly lower in patients with HEG and demonstrated a strong negative correlation with disease severity. Both NLR and SII were positively correlated with PUQE scores. ROC analysis revealed that NLR and MLR had moderate diagnostic accuracy for HEG. In the multivariable logistic regression analysis, only NLR was identified as an independent risk factor for HEG development. The results indicate that hematological inflammatory indices, particularly the NLR and HALP score, are significantly altered in HEG. Although their discriminative ability was only moderate, they may be considered easily accessible and supportive indicators rather than stand-alone diagnostic tools. Further prospective multicenter studies are needed to confirm the clinical utility of these biomarkers in the management of hyperemesis gravidarum.