Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification

Emotion detection in textual data has received growing interest in recent years, as it is pivotal for developing empathetic human-computer interaction systems. This paper introduces a method for categorizing emotions from text, which acknowledges and differentiates between the diversified similarities and distinctions of various emotions. Initially, we establish a baseline by training a transformer-based model for standard emotion classification, achieving state-of-the-art performance. We argue that not all misclassifications are of the same importance, as there are perceptual similarities among emotional classes. We thus redefine the emotion labeling problem by shifting it from a traditional classification model to an ordinal classification one, where discrete emotions are arranged in a sequential order according to their valence levels. Finally, we propose a method that performs ordinal classification in the two-dimensional emotion space, considering both valence and arousal scales. The results show that our approach not only preserves high accuracy in emotion prediction but also significantly reduces the magnitude of errors in cases of misclassification

Lesion Size Is Exacerbated in Hypoxic Rats Whereas Hypoxia-Inducible Factor-1 Alpha and Vascular Endothelial Growth Factor Increase in Injured Normoxic Rats: A Prospective Cohort Study of Secondary Hypoxia in Focal Traumatic Brain Injury

BACKGROUND: Hypoxia following traumatic brain injury (TBI) is a severe insult shown to exacerbate the pathophysiology, resulting in worse outcome. The aim of this study was to investigate the effects of a hypoxic insult in a focal TBI model by monitoring brain edema, lesion volume, serum biomarker levels, immune cell infiltration, as well as the expression of hypoxia-inducible factor-1 alpha (HIF-1α) and vascular endothelial growth factor (VEGF). MATERIALS AND METHODS: Female Sprague-Dawley rats (n = 73, including sham and naive) were used. The rats were intubated and mechanically ventilated. A controlled cortical impact device created a 3-mm deep lesion in the right parietal hemisphere. Post-injury, rats inhaled either normoxic (22% O(2)) or hypoxic (11% O(2)) mixtures for 30 min. The rats were sacrificed at 1, 3, 7, 14, and 28 days post-injury. Serum was collected for S100B measurements using ELISA. Ex vivo magnetic resonance imaging (MRI) was performed to determine lesion size and edema volume. Immunofluorescence was employed to analyze neuronal death, changes in cerebral macrophage- and neutrophil infiltration, microglia proliferation, apoptosis, complement activation (C5b9), IgG extravasation, HIF-1α, and VEGF. RESULTS: The hypoxic group had significantly increased blood levels of lactate and decreased pO(2) (p < 0.0001). On MRI post-traumatic hypoxia resulted in larger lesion areas (p = 0.0173), and NeuN staining revealed greater neuronal loss (p = 0.0253). HIF-1α and VEGF expression was significantly increased in normoxic but not in hypoxic animals (p < 0.05). A trend was seen for serum levels of S100B to be higher in the hypoxic group at 1 day after trauma (p = 0.0868). No differences were observed between the groups in cytotoxic and vascular edema, IgG extravasation, neutrophils and macrophage aggregation, microglia proliferation, or C5b-9 expression. CONCLUSION: Hypoxia following focal TBI exacerbated the lesion size and neuronal loss. Moreover, there was a tendency to higher levels of S100B in the hypoxic group early after injury, indicating a potential validity as a biomarker of injury severity. In the normoxic group, the expression of HIF-1α and VEGF was found elevated, possibly indicative of neuro-protective responses occurring in this less severely injured group. Further studies are warranted to better define the pathophysiology of post-TBI hypoxia

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.