Cross-Domain Few-Shot Classification via Inter-Source Stylization

The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary dataset, despite the differences between the domains of the two datasets. Some existing approaches require labelled samples from multiple domains for model training. However, these methods fail when the sample labels are scarce. To overcome this challenge, this paper proposes a solution that makes use of multiple source domains without the need for additional labeling costs. Specifically, one of the source domains is completely tagged, while the others are untagged. An Inter-Source Stylization Network (ISSNet) is then introduced to enhance stylisation across multiple source domains, enriching data distribution and model's generalization capabilities. Experiments on 8 target datasets show that ISSNet leverages unlabelled data from multiple source data and significantly reduces the negative impact of domain gaps on classification performance compared to several baseline methods.Comment: 5 page

Unbiased Scene Graph Generation via Two-stage Causal Modeling

Despite the impressive performance of recent unbiased Scene Graph Generation (SGG) methods, the current debiasing literature mainly focuses on the long-tailed distribution problem, whereas it overlooks another source of bias, i.e., semantic confusion, which makes the SGG model prone to yield false predictions for similar relationships. In this paper, we explore a debiasing procedure for the SGG task leveraging causal inference. Our central insight is that the Sparse Mechanism Shift (SMS) in causality allows independent intervention on multiple biases, thereby potentially preserving head category performance while pursuing the prediction of high-informative tail relationships. However, the noisy datasets lead to unobserved confounders for the SGG task, and thus the constructed causal models are always causal-insufficient to benefit from SMS. To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages. The first stage is causal representation learning, where we use a novel Population Loss (P-Loss) to intervene in the semantic confusion confounder. The second stage introduces the Adaptive Logit Adjustment (AL-Adjustment) to eliminate the long-tailed distribution confounder to complete causal calibration learning. These two stages are model agnostic and thus can be used in any SGG model that seeks unbiased predictions. Comprehensive experiments conducted on the popular SGG backbones and benchmarks show that our TsCM can achieve state-of-the-art performance in terms of mean recall rate. Furthermore, TsCM can maintain a higher recall rate than other debiasing methods, which indicates that our method can achieve a better tradeoff between head and tail relationships.Comment: 17 pages, 9 figures. Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligenc

Research progress on moyamoya disease combined with thyroid diseases

Moyamoya disease (MMD), also known as abnormal cerebral vascular network disease, is characterized by progressive occlusion or stenosis of the internal carotid and cerebral arteries, as well as the formation of an abnormal cerebral vascular network. It can occur anywhere in the world but is most common in China, Japan, and the Republic of Korea. In recent years, there have been increasing reports on the coexistence of thyroid diseases and MMD, but the mechanism of their coexistence is still unclear. For this article, we used keywords such as “moyamoya disease”, “thyroid”, “Grave disease”, “thyrotoxicosis”, and “thyroid autoimmune antibodies” to search for 52 articles that met the requirements in medical databases such as PubMed and Web of Science. This article also reviews the research on the role of thyroid hormone, the mechanism of immune antibodies, the possible correlation between thyroid diseases and MMD disease genes, and the treatment methods, and discusses the possible relationship between MMD and thyroid diseases to provide a reference for the pathogenesis and treatment of MMD with thyroid diseases

Sliding-MOMP Based Channel Estimation Scheme for ISDB-T Systems

Compressive sensing based channel estimation has shown its advantage of accurate reconstruction for sparse signal with less pilots for OFDM systems. However, high computational cost requirement of CS method, due to linear programming, significantly restricts its implementation in practical applications. In this paper, we propose a reduced complexity channel estimation scheme of modified orthogonal matching pursuit with sliding windows for ISDB-T (Integrated Services Digital Broadcasting for Terrestrial) system. The proposed scheme can reduce the computational cost by limiting the searching region as well as making effective use of the last estimation result. In addition, adaptive tracking strategy with sliding sampling window can improve the robustness of CS based methods to guarantee its accuracy of channel matrix reconstruction, even for fast time-variant channels. The computer simulation demonstrates its impact on improving bit error rate and computational complexity for ISDB-T system

Effects of plant diversity and big-sized trees on ecosystem function in a tropical montane evergreen broad-leaved forest

IntroductionScale dependencies play a vital role in defining the biodiversity-ecosystem functioning relationship in forest ecosystems, which varies based on the magnitude of multiple plant diversity attributes, soil properties, and aboveground biomass in forest ecosystems. However, the effects of plant diversity and big-sized trees on the relationship between plant diversity and aboveground biomass across different scales remain unclear in forest ecosystems.MethodsBased on a 30-ha tropical montane evergreen broad-leaved forest dynamics plot in Yunnan province, China, we comparatively analyzed the importance of scale-dependent effects of multiple plant diversity attributes, soil properties, neighborhood competition intensity and aboveground biomass of big-sized trees, as well as stand structural complexity on aboveground biomass of all woody individuals. The aim is therefore to identify the main predictors for sustaining aboveground biomass of all woody individuals, considering multiple biotic and abiotic factors jointly, as well as underlying mechanisms.ResultsOur results suggest that indicators such as species richness and phylogenetic diversity did not strongly contribute to aboveground biomass of all woody individuals with increasing spatial scales, while aboveground biomass of big-sized trees exhibited the greatest contribution to aboveground biomass of all woody individuals. Stand structural complexity, characterized by variances in woody plant diameter at breast height, also contributed more to aboveground biomass of all woody individuals indirectly via neighborhood competition intensity and aboveground biomass of big-sized trees. Contributions of functional dispersion and community-weighted mean of leaf phosphorus concentration to aboveground biomass of all woody individuals became stronger with increasing spatial scales. Neighborhood competition intensity exhibited a negative linear relationship with aboveground biomass of all woody individuals at the smallest scale, but it affected positively aboveground biomass of all woody individuals across spatial scales, likely due to indirect effects via aboveground biomass of big-sized trees.DiscussionBig-sized trees will likely become more important in biodiversity maintenance and ecosystem function management as deforestation and forest degradation

Greater Pain Severity Is Associated With Higher Glucocorticoid Levels in Hair Among a Cohort of People Living With HIV

Background: Pain is a common occurrence and persistent symptom, which has an adverse impact on individual well-being and quality of life among people living with HIV (PLHIV). Alteration in the activity of the Hypothalamic-Pituitary-Adrenal (HPA) axis resulting in abnormal glucocorticoid levels had been proposed to play important roles in those associations. Purpose: This study aimed to investigate whether pain severity was associated with hair glucocorticoid levels, a novel method of measuring long-term glucocorticoid exposure, among a large cohort of Chinese PLHIV. Methods: A measure of pain severity and hair samples were collected from 431 adults PLHIV in Guangxi, China. Glucocorticoid (cortisol and cortisone) in hair were quantified by liquid chromatography-tandem mass spectrometry. The general linear model was used to test the associations of pain severity with hair glucocorticoid levels after adjusting for potential confounding factors. Results: Of the 431 PLHIV, 273 reported none pain, 87 reported mild pain, and 71 reported moderate-severe pain. Hair cortisone, but not hair cortisol, was found to differ significantly among the three pain severity groups (F=3.90, p=0.021). PLHIV reported moderate-severe pain had higher hair cortisone than those reported mild (p=0.070) or none pain (p=0.014), with no differences between the latter two pain severity groups. Conclusion: Greater pain severity is associated with higher hair cortisone levels among Chinese PLHIV. In order to reduce the long-term glucocorticoid levels, interventions managing pain should be considered for PLHIV with moderate-severe pain

The Relationship of Hair Glucocorticoid Levels to Immunological and Virological Outcomes in a Large Cohort of Combination Antiretroviral Therapy Treated People Living With HIV

Background Existing literature mostly investigated the relationship of acute or short-term glucocorticoid exposure to HIV disease progression using cortisol levels in serum, saliva, or urine. Data are limited on the relationship of long-term glucocorticoid exposure to HIV disease progression. This study examined whether hair glucocorticoid levels, novel retrospective indicators of long-term glucocorticoid exposure, are associated with two common indicators of HIV disease progression (CD4 count and HIV viral load) among a large cohort of combination antiretroviral therapy treated Chinese people living with HIV (PLHIV). Methods A total of 1198 treated PLHIV provided hair samples for glucocorticoid (cortisol and cortisone) assay and completed a survey assessing sociodemographic, lifestyle, and HIV-related characteristics. Meanwhile, CD4 count and HIV viral load were retrieved from their medical records. Spearman correlation was used to examine the associations of hair cortisol and cortisone levels to continuous CD4 count and HIV viral load. Multivariate logistic regression was used to predict CD4 count \u3c 500 cells/mm3. Results Both hair cortisol and cortisone levels were negatively associated with CD4 count but not with HIV viral load. The odds ratio for CD4 count \u3c 500 cells/mm3 was 1.41 [95% CI 0.99–2.00] and 2.15 [95% CI 1.51–3.05] for those with hair cortisol and cortisone levels in the highest quartile compared to the lowest when controlling for sociodemographic, lifestyle, HIV-related covariates, and HIV viral load. Conclusion Hair glucocorticoid levels were associated with CD4 count but not viral load in treated Chinese PLHIV. Our data furtherly supported the hypothesis that elevated glucocorticoid levels are associated with the lower CD4 count

Association of Hair Concentrations of Antiretrovirals With Virologic Outcomes Among People Living With HIV in Guangxi, China

Background: Hair concentrations of antiretrovirals are an innovative and non-invasive method for measuring cumulative antiretroviral exposure and assessing long-term antiretroviral adherence. This study aimed to examine hair concentrations of antiretrovirals in relation to virologic outcomes among PLHIV in Guangxi, China.Methods: Cross-sectional data of hair concentrations of antiretrovirals and HIV viral load were collected from 215 PLHIV in Guangxi, China. Multivariate logistic regression analyses were used to examine the association of hair concentrations of antiretrovirals with virologic outcomes.Results: Of the 215 participants, 215, 67, and 163 PLHIV are receiving lamivudine, zidovudine, and efavirenz, respectively. Multivariate analysis revealed that hair concentrations of lamivudine [odds ratio = 16.52, 95% CI 2.51– 108.60, p = 0.004] and efavirenz [odds ratio = 14.26, 95% CI 1.18– 172.01, p = 0.036], but not zidovudine [odds ratio = 1.77, 95% CI 0.06– 56.14, p = 0.747], were the strongest independent predictor of virologic suppression when controlling for sociodemographic and other HIV-related characteristics.Conclusion: Hair concentrations of lamivudine and efavirenz were the strongest independent predictor of virologic suppression among Chinese PLHIV. Hair analysis of antiretrovirals may provide a non-invasive, cost-effective tool that predicts virologic suppression among PLHIV in China

Clinical value of the systemic immune-inflammation index in moyamoya disease

BackgroundMoyamoya disease (MMD) is a rare cerebrovascular disorder with unknown etiology. The underlying pathophysiological mechanism of moyamoya disease remains to be elucidated, but recent studies have increasingly highlighted that abnormal immune response may be a potential trigger for MMD. Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) are inflammatory markers that can reflect the immune-inflammation state of the disease.ObjectiveThe purpose of this study was to investigate SII, NLR, and PLR in patients with moyamoya disease.MethodsA total of 154 patients with moyamoya disease (MMD group) and 321 age- and sex-matched healthy subjects (control group) were included in this retrospective case–control study. Complete blood count parameters were assayed to calculate the SII, NLR, and PLR values.ResultsThe SII, NLR, and PLR values in the moyamoya disease group were significantly higher than those in the control group [754 ± 499 vs. 411 ± 205 (P < 0.001), 2.83 ± 1.98 vs. 1.81 ± 0.72 (P < 0.001), and 152 ± 64 vs. 120 ± 42 (P < 0.001), respectively]. The SII in the medium-moyamoya vessels of moyamoya disease was higher than that in the high-moyamoya vessels and low-moyamoya vessels (P = 0.005). Using the receiver operating characteristic (ROC) curve analysis to predict MMD, the highest area under the curve (AUC) was determined for SII (0.76 for SII, 0.69 for NLR, and 0.66 for PLR).ConclusionBased on the results of this study, patients with moyamoya disease admitted for inpatient care due to acute or chronic stroke have significantly higher SII, NLR, and PLR when compared to blood samples drawn from completely healthy controls in a non-emergent outpatient setting. While the findings may suggest that inflammation plays a role in moyamoya disease, further studies are warranted to corroborate such an association. In the middle stage of moyamoya disease, there may be a more intense imbalance of immune inflammation. Further studies are needed to determine whether the SII index contributes to the diagnosis or serves as a potential marker of an inflammatory response in patients with moyamoya disease