Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling

While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained than that of image-language models; existing practices of temporal modeling also suffer from weak and noisy alignment between modalities. To learn fine-grained visual understanding, we decouple spatial-temporal modeling and propose a hybrid pipeline, Decoupled Spatial-Temporal Encoders, integrating an image- and a video-language encoder. The former encodes spatial semantics from larger but sparsely sampled frames independently of time, while the latter models temporal dynamics at lower spatial but higher temporal resolution. To help the video-language model learn temporal relations for video QA, we propose a novel pre-training objective, Temporal Referring Modeling, which requires the model to identify temporal positions of events in video sequences. Extensive experiments demonstrate that our model outperforms previous work pre-trained on orders of magnitude larger datasets.Comment: BMVC 2022. Code is available at https://github.com/shinying/des

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO , to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment

Intensify the application of ZnO-based nanodevices in humid environment: O2/H2 plasma suppressed the spontaneous reaction of amorphous ZnO nanowires

[[abstract]]In this work, we have demonstrated that amorphous ZnO nanobranches (a-ZnO NBs) could spontaneously react from the crystalline ZnO NWs (c-ZnO NWs) at specific humid environment. The spontaneous reaction mechanism and result can be analyzed by humidity controlling and optical microscope (OM)/scanning electron microscope (SEM)/Kelvin probe force microscopy (KPFM)/transmission electron microscopy (TEM) system. We can make the c-ZnO NWs spontaneous reaction happen at different humid environments and suppress the a-ZnO NBs spontaneous reaction by oxygen/hydrogen plasma surface passivation. The hydrogen plasma surface treatment also can improve the UV sensing sensitivity more than twofold. This work provides the mechanism and methods of the a-ZnO NBs spontaneous growth and offers the passivation treatment for strengthening and enhancing ZnO-based nanodevice application in humid environment and UV light detection, respectively.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[booktype]]紙本[[countrycodes]]DE

BIOSMILE: A semantic role labeling system for biomedical verbs using a maximum-entropy model with automatically generated template features

Background: Bioinformatics tools for automatic processing of biomedical literature are invaluable for both the design and interpretation of large-scale experiments. Many information extraction (IE) systems that incorporate natural language processing (NLP) techniques have thus been developed for use in the biomedical field. A key IE task in this field is the extraction of biomedical relations, such as protein-protein and gene-disease interactions. However, most biomedical relation extraction systems usually ignore adverbial and prepositional phrases and words identifying location, manner, timing, and condition, which are essential for describing biomedical relations. Semantic role labeling (SRL) is a natural language processing technique that identifies the semantic roles of these words or phrases in sentences and expresses them as predicate-argument structures. We construct a biomedical SRL system called BIOSMILE that uses a maximum entropy (ME) machine-learning model to extract biomedical relations. BIOSMILE is trained on BioProp, our semi-automatic, annotated biomedical proposition bank. Currently, we are focusing on 30 biomedical verbs that are frequently used or considered important for describing molecular events. Results: To evaluate the performance of BIOSMILE, we conducted two experiments to (1) compare the performance of SRL systems trained on newswire and biomedical corpora; and (2) examine the effects of using biomedical-specific features. The experimental results show that using BioProp improves the F-score of the SRL system by 21.45% over an SRL system that uses a newswire corpus. It is noteworthy that adding automatically generated template features improves the overall F-score by a further 0.52%. Specifically, ArgM-LOC, ArgM-MNR, and Arg2 achieve statistically significant performance improvements of 3.33%, 2.27%, and 1.44%, respectively. Conclusion: We demonstrate the necessity of using a biomedical proposition bank for training SRL systems in the biomedical domain. Besides the different characteristics of biomedical and newswire sentences, factors such as cross-domain framesets and verb usage variations also influence the performance of SRL systems. For argument classification, we find that NE (named entity) features indicating if the target node matches with NEs are not effective, since NEs may match with a node of the parsing tree that does not have semantic role labels in the training set. We therefore incorporate templates composed of specific words, NE types, and POS tags into the SRL system. As a result, the classification accuracy for adjunct arguments, which is especially important for biomedical SRL, is improved significantly

Association of Female Menopause With Atrioventricular Mechanics and Outcomes

BACKGROUND: Despite known sex differences in cardiac structure and function, little is known about how menopause and estrogen associate with atrioventricular mechanics and outcomes. OBJECTIVE: To study how, sex differences, loss of estrogen in menopause and duration of menopause, relate to atrioventricular mechanics and outcomes. METHODS: Among 4051 asymptomatic adults (49.8 ± 10.8 years, 35%women), left ventricular (LV) and left atrial (LA) mechanics were assessed using speckle-tracking. RESULTS: Post-menopausal (vs. pre-menopausal) women had similar LV ejection fraction but reduced GLS, reduced PALS, increased LA stiffness, higher LV sphericity and LV torsion (all p < 0.001). Multivariable analysis showed menopause to be associated with greater LV sphericity (0.02, 95%CI 0.01, 0.03), higher indexed LV mass (LVMi), lower mitral e’, lower LV GLS (0.37, 95%CI 0.04–0.70), higher LV torsion, larger LA volume, worse PALS (∼2.4-fold) and greater LA stiffness (0.028, 95%CI 0.01–0.05). Increasing years of menopause was associated with further reduction in GLS, markedly worse LA mechanics despite greater LV sphericity and higher torsion. Lower estradiol levels correlated with more impaired LV diastolic function, impaired LV GLS, greater LA stiffness, and increased LV sphericity and LV torsion (all p < 0.05). Approximately 5.5% (37/669) of post-menopausal women incident HF over 2.9 years of follow-up. Greater LV sphericity [adjusted hazard ratio (aHR) 1.04, 95%CI 1.00–1.07], impaired GLS (aHR 0.87, 95%CI 0.78–0.97), reduced peak left atrial longitudinal strain (PALS, aHR 0.94, 95%CI 0.90–0.99) and higher LA stiffness (aHR 10.5, 95%CI 1.69–64.6) were independently associated with the primary outcome of HF hospitalizations in post-menopause. Both PALS < 23% (aHR:1.32, 95%CI 1.01–3.49) and GLS < 16% (aHR:5.80, 95%CI 1.79–18.8) remained prognostic for the incidence of HF in post-menopausal women in dichotomous analyses, even after adjusting for confounders. Results were consistent with composite outcomes of HF hospitalizations and 1-year all-cause mortality as well. CONCLUSION: Menopause was associated with greater LV/LA remodeling and reduced LV longitudinal and LA function in women. The cardiac functional deficit with menopause and lower estradiol levels, along with their independent prognostic value post-menopause, may elucidate sex differences in heart failure further

Detection of SARS-associated Coronavirus in Throat Wash and Saliva in Early Diagnosis

Early detection of SARS-CoV in throat wash and saliva suggests that these specimens are ideal for SARS diagnosis

An examination of cancer-related fatigue through proposed diagnostic criteria in a sample of cancer patients in Taiwan

<p>Abstract</p> <p>Background</p> <p>Fatigue among cancer patients has often been reported in the literature; however, great variations have been documented, ranging from 15% to 90%, probably due to the lack of a widely accepted definition and established diagnostic criteria for cancer-related fatigue. The objective of this study was to evaluate the proposed International Statistical Classification of Diseases and Related Health Problems (10^threvision) (ICD-10) criteria in a sample of cancer patients from a medical center and a regional teaching hospital in northern Taiwan. More accurate prevalence estimates of CRF may result in improved diagnoses and management of one of the most common symptoms associated with cancer and its treatment.</p> <p>Methods</p> <p>Since self-reporting from patients is the most effective and efficient method to measure fatigue, the ICD-10 criteria for fatigue were used. The ICD-10 criteria questionnaire was translated into Chinese and was approved by experts. Patients were recruited from outpatient palliative and oncology clinics and from palliative and oncology inpatient units.</p> <p>Results</p> <p>Of the 265 cancer patients that were interviewed between 21 October 2008 and 28 October 2009, 228 (86%) reported having at least 2 weeks of fatigue in the past month, and further evaluation with the ICD-10 criteria showed that 132 (49.8%) had cancer-related fatigue. Internal consistency was very good, which was indicated by a Cronbach alpha of 0.843.</p> <p>Conclusion</p> <p>The prevalence of diagnosable CRF in the patients in this sample, of whom most were under palliative treatment, was 49.8%, which was probably somewhat lower than in some of the previous reports that have used less-strict criteria. In addition, among the various criteria of the proposed diagnostic criteria, the most frequently reported symptoms in our sample populations were regarding sleep disturbance and physical factors. Although they will require further replication in other samples, these formal diagnostic criteria can serve as a step toward a common language and a better understanding of the severity range of CRF.</p

Acute kidney injury in patients with COVID-19 compared to those with influenza: a systematic review and meta-analysis

BackgroundCOVID-19 and influenza can both lead to acute kidney injury (AKI) as a common complication. However, no meta-analysis has been conducted to directly compare the incidence of AKI between hospitalized patients with COVID-19 and influenza. The objective of our study aims to investigate the incidence and outcomes of AKI among hospitalized patients between these two groups.Materials and methodsA systematic search of PubMed, Embase, and Cochrane databases was conducted from December 2019 to August 2023 to identify studies examining AKI and clinical outcomes among hospitalized patients with COVID-19 and influenza. The primary outcome of interest was the incidence of AKI, while secondary outcomes included in-hospital mortality, recovery from AKI, hospital and ICU stay duration. The quality of evidence was evaluated using Cochrane and GRADE methods.ResultsTwelve retrospective cohort studies, involving 17,618 hospitalized patients with COVID-19 and influenza, were analyzed. COVID-19 patients showed higher AKI incidence (29.37% vs. 20.98%, OR: 1.67, 95% CI 1.56–1.80, p &lt; 0.01, I2 = 92.42%), and in-hospital mortality (30.95% vs. 5.51%, OR: 8.16, 95% CI 6.17–10.80, p &lt; 0.01, I2 = 84.92%) compared to influenza patients with AKI. Recovery from AKI was lower in COVID-19 patients (57.02% vs., 80.23%, OR: 0.33, 95% CI 0.27–0.40, p &lt; 0.01, I2 = 85.17%). COVID-19 patients also had a longer hospital stay (SMD: 0.69, 95% CI 0.65–0.72, p &lt; 0.01, I2 = 98.94%) and longer ICU stay (SMD: 0.61, 95% CI 0.50–0.73, p &lt; 0.01, I2 = 94.80%) than influenza patients. In our study, evidence quality was high (NOS score 7–9), with low certainty for AKI incidence and moderate certainty for recovery form AKI by GRADE assessment.ConclusionCOVID-19 patients had higher risk of developing AKI, experiencing in-hospital mortality, and enduring prolonged hospital/ICU stays in comparison to influenza patients. Additionally, the likelihood of AKI recovery was lower among COVID-19 patients