Performance of a new Candida anti-mannan IgM and IgG assays in the diagnosis of candidemia

Candida is one of the most frequent pathogens of bloodstream infections, which is associated with high morbidity and mortality rates. Rapid immunological detection methods are essential in the early diagnosis of candidemia. Anti-mannan is one of host-derived biomarkers against cell wall components of Candida. We conducted this study to evaluate the diagnostic performance of two anti-mannan assays (IgM, IgG) for candidemia through the analysis of 40 candidemia patients, 48 participants with Candida colonization and 213 participants with neither Candida colonization nor Candida infections (13 patients with other bloodstream infections, 145 hospitalized patients and 55 healthy controls). The performance of the two assays were evaluated by calculating their sensitivity and specificity. The sensitivity ranged from 0.78 to 0.80 for the IgM assay and 0.68 to 0.75 for the IgG assay. The specificity ranged from 0.97 to 0.98 for the IgM assay and 0.91 to 0.94 for the IgG assay. The diagnostic performance of the anti-mannan IgM assay was better than that of IgG, with higher sensitivity and specificity. Combining the two assays (positive results of single or both assays are both considered as positive) could improve the sensitivity up to 0.93 (0.79-0.98) and only slightly reduce the specificity (0.93(0.89-0.95)). The anti-mannan IgM, IgG assays are rapid and cost-effective assays that may be probably useful in the diagnosis of candidemia

FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling

With the availability of large-scale video datasets and the advances of diffusion models, text-driven video generation has achieved substantial progress. However, existing video generation models are typically trained on a limited number of frames, resulting in the inability to generate high-fidelity long videos during inference. Furthermore, these models only support single-text conditions, whereas real-life scenarios often require multi-text conditions as the video content changes over time. To tackle these challenges, this study explores the potential of extending the text-driven capability to generate longer videos conditioned on multiple texts. 1) We first analyze the impact of initial noise in video diffusion models. Then building upon the observation of noise, we propose FreeNoise, a tuning-free and time-efficient paradigm to enhance the generative capabilities of pretrained video diffusion models while preserving content consistency. Specifically, instead of initializing noises for all frames, we reschedule a sequence of noises for long-range correlation and perform temporal attention over them by window-based function. 2) Additionally, we design a novel motion injection method to support the generation of videos conditioned on multiple text prompts. Extensive experiments validate the superiority of our paradigm in extending the generative capabilities of video diffusion models. It is noteworthy that compared with the previous best-performing method which brought about 255% extra time cost, our method incurs only negligible time cost of approximately 17%. Generated video samples are available at our website: http://haonanqiu.com/projects/FreeNoise.html.Comment: Project Page: http://haonanqiu.com/projects/FreeNoise.html Code Repo: https://github.com/arthur-qiu/LongerCrafte

Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference

The capability to generate responses with diversity and faithfulness using factual knowledge is paramount for creating a human-like, trustworthy dialogue system. Common strategies either adopt a two-step paradigm, which optimizes knowledge selection and response generation separately, and may overlook the inherent correlation between these two tasks, or leverage conditional variational method to jointly optimize knowledge selection and response generation by employing an inference network. In this paper, we present an end-to-end learning framework, termed Sequential Posterior Inference (SPI), capable of selecting knowledge and generating dialogues by approximately sampling from the posterior distribution. Unlike other methods, SPI does not require the inference network or assume a simple geometry of the posterior distribution. This straightforward and intuitive inference procedure of SPI directly queries the response generation model, allowing for accurate knowledge selection and generation of faithful responses. In addition to modeling contributions, our experimental results on two common dialogue datasets (Wizard of Wikipedia and Holl-E) demonstrate that SPI outperforms previous strong baselines according to both automatic and human evaluation metrics

HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting

Realistic 3D human generation from text prompts is a desirable yet challenging task. Existing methods optimize 3D representations like mesh or neural fields via score distillation sampling (SDS), which suffers from inadequate fine details or excessive training time. In this paper, we propose an efficient yet effective framework, HumanGaussian, that generates high-quality 3D humans with fine-grained geometry and realistic appearance. Our key insight is that 3D Gaussian Splatting is an efficient renderer with periodic Gaussian shrinkage or growing, where such adaptive density control can be naturally guided by intrinsic human structures. Specifically, 1) we first propose a Structure-Aware SDS that simultaneously optimizes human appearance and geometry. The multi-modal score function from both RGB and depth space is leveraged to distill the Gaussian densification and pruning process. 2) Moreover, we devise an Annealed Negative Prompt Guidance by decomposing SDS into a noisier generative score and a cleaner classifier score, which well addresses the over-saturation issue. The floating artifacts are further eliminated based on Gaussian size in a prune-only phase to enhance generation smoothness. Extensive experiments demonstrate the superior efficiency and competitive quality of our framework, rendering vivid 3D humans under diverse scenarios. Project Page: https://alvinliu0.github.io/projects/HumanGaussianComment: Accepted by CVPR 2024, camera-ready version. Project Page: https://alvinliu0.github.io/projects/HumanGaussia

Adversarially Robust Neural Architecture Search for Graph Neural Networks

Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk-sensitive domains. Existing defensive methods neither guarantee performance facing new data/tasks or adversarial attacks nor provide insights to understand GNN robustness from an architectural perspective. Neural Architecture Search (NAS) has the potential to solve this problem by automating GNN architecture designs. Nevertheless, current graph NAS approaches lack robust design and are vulnerable to adversarial attacks. To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA). Specifically, we design a robust search space for the message-passing mechanism by adding graph structure mask operations into the search space, which comprises various defensive operation candidates and allows us to search for defensive GNNs. Furthermore, we define a robustness metric to guide the search procedure, which helps to filter robust architectures. In this way, G-RNA helps understand GNN robustness from an architectural perspective and effectively searches for optimal adversarial robust GNNs. Extensive experimental results on benchmark datasets show that G-RNA significantly outperforms manually designed robust GNNs and vanilla graph NAS baselines by 12.1% to 23.4% under adversarial attacks.Comment: Accepted as a conference paper at CVPR 202

A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023

In this technical report, we present our findings from a study conducted on the EPIC-KITCHENS-100 Unsupervised Domain Adaptation task for Action Recognition. Our research focuses on the innovative application of a differentiable logic loss in the training to leverage the co-occurrence relations between verb and noun, as well as the pre-trained Large Language Models (LLMs) to generate the logic rules for the adaptation to unseen action labels. Specifically, the model's predictions are treated as the truth assignment of a co-occurrence logic formula to compute the logic loss, which measures the consistency between the predictions and the logic constraints. By using the verb-noun co-occurrence matrix generated from the dataset, we observe a moderate improvement in model performance compared to our baseline framework. To further enhance the model's adaptability to novel action labels, we experiment with rules generated using GPT-3.5, which leads to a slight decrease in performance. These findings shed light on the potential and challenges of incorporating differentiable logic and LLMs for knowledge extraction in unsupervised domain adaptation for action recognition. Our final submission (entitled `NS-LLM') achieved the first place in terms of top-1 action recognition accuracy.Comment: Technical report submitted to CVPR 2023 EPIC-Kitchens challenge

Rhein targets macrophage SIRT2 to promote adipose tissue thermogenesis in obesity in mice.

Rhein, a component derived from rhubarb, has been proven to possess anti-inflammatory properties. Here, we show that rhein mitigates obesity by promoting adipose tissue thermogenesis in diet-induced obese mice. We construct a macrophage-adipocyte co-culture system and demonstrate that rhein promotes adipocyte thermogenesis through inhibiting NLRP3 inflammasome activation in macrophages. Moreover, clues from acetylome analysis identify SIRT2 as a potential drug target of rhein. We further verify that rhein directly interacts with SIRT2 and inhibits NLRP3 inflammasome activation in a SIRT2-dependent way. Myeloid knockdown of SIRT2 abrogates adipose tissue thermogenesis and metabolic benefits in obese mice induced by rhein. Together, our findings elucidate that rhein inhibits NLRP3 inflammasome activation in macrophages by regulating SIRT2, and thus promotes white adipose tissue thermogenesis during obesity. These findings uncover the molecular mechanism underlying the anti-inflammatory and anti-obesity effects of rhein, and suggest that rhein may become a potential drug for treating obesity

Extraction, Component Analysis and Biological Activity Evaluation of Total Flavonoids from Phellinus igniarius

In order to study the extraction technology of total flavonoids from Phellinus igniarius systematically, clarify the composition and content of flavonoids in total flavonoids, and explore the biological activity of total flavonoids from Phellinus igniarius. The extraction technology of Phellinus igniarius total flavonoids was optimized by response surface method, the composition of Phellinus igniarius total flavonoids was analyzed, and some of its biological activities were also detected. The results showed that the optimal extraction conditions were extraction temperature of 73 ℃, solid-liquid ratio of 1:50 g/mL and extraction time of 3 h, under which the yield of Phellinus igniarius total flavonoids reached 2.68%. The types and contents of flavonoids in Phellinus igniarius total flavonoids were determined by HPLC. Total flavonoids of Phellinus igniarius mainly contained taxifolin, quercetin and kaempferol, and the content of taxifolin was 3727.31 mg/kg. The analysis of biological activities of Phellinus igniarius total flavonoids showed that it had certain antioxidant, lipid-lowering and hypoglycemic activities in vitro, and the scavenging rate of DPPH free radicals reached 58.63%±0.45% when its concentration was 14 μg/mL, and the scavenging rate of hydroxyl free radicals reached 52.51%±1.49% when its concentration was 0.08 mg/mL. The experimental results of lipid-lowering activity showed that the inhibitory rate of Phellinus igniarius total flavonoids on pancreatic lipase reached 10.56%±0.06% at the concentration of 6 mg/mL, and the inhibitory rate on cholesterol micelle solubility reached 32.59%±0.78% at the concentration of 4 mg/mL. Phellinus igniarius total flavonids had obvious hypoglycemic activity, and its IC50 for α-glucosidase and α-amylase was 71.42 mg/mL and 97.28 mg/mL respectively

Prediction and analysis of components and functions of Ixeris chinensis based on network pharmacology and molecular docking

BackgroundIt is reported that the Ixeris chinensis has high medicinal value, but there are few reports about its potential molecular mechanism. We used a network pharmacology approach to predict the active ingredients, targets of action and possible interventions in diseases of Ixeris chinensis.MethodsWe employed various databases and software to predict the active ingredients, target genes, protein interactions, signaling pathways, network diagrams, and molecular docking of Ixeris chinensis. Simultaneously, we searched multiple Chinese and English databases and conducted meta-analyses of five randomized controlled trials.ResultsThe analysis results revealed 12 effective components, including apigenin β-sitosterol, baicalin, baicalein, and luteolin; and selected 40 key targets, including AKT1, TNF, EGFR, ESR1, SRC, among others. GO analysis generated 225 biological processes, 39 cellular components, and 65 molecular functions; KEGG analysis revealed 103 signaling pathways. Molecular docking results indicated that the main active components of Ixeris chinensis can bind well with key targets. Five randomized controlled trials were included. Meta-analysis showed that Ixeris extract can effectively reduce animal blood lipid levels.ConclusionThis study revealed the main active ingredients and key targets of Ixeris chinensis, analyzed the signaling pathways of potential targets, conducted disease prediction, and performed molecular docking prediction, providing a basis for research on the pathways of Ixeris treatment for related diseases and subsequent new drug development

Microglia lactylation in relation to central nervous system diseases

The development of neurodegenerative diseases is closely related to the disruption of central nervous system homeostasis. Microglia, as innate immune cells, play important roles in the maintenance of central nervous system homeostasis, injury response, and neurodegenerative diseases. Lactate has been considered a metabolic waste product, but recent studies are revealing ever more of the physiological functions of lactate. Lactylation is an important pathway in lactate function and is involved in glycolysis-related functions, macrophage polarization, neuromodulation, and angiogenesis and has also been implicated in the development of various diseases. This review provides an overview of the lactate metabolic and homeostatic regulatory processes involved in microglia lactylation, histone versus non-histone lactylation, and therapeutic approaches targeting lactate. Finally, we summarize the current research on microglia lactylation in central nervous system diseases. A deeper understanding of the metabolic regulatory mechanisms of microglia lactylation will provide more options for the treatment of central nervous system diseases