One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls

It is well known that many open-released foundational diffusion models have difficulty in generating images that substantially depart from average brightness, despite such images being present in the training data. This is due to an inconsistency: while denoising starts from pure Gaussian noise during inference, the training noise schedule retains residual data even in the final timestep distribution, due to difficulties in numerical conditioning in mainstream formulation, leading to unintended bias during inference. To mitigate this issue, certain $\epsilon$-prediction models are combined with an ad-hoc offset-noise methodology. In parallel, some contemporary models have adopted zero-terminal SNR noise schedules together with $\mathbf{v}$-prediction, which necessitate major alterations to pre-trained models. However, such changes risk destabilizing a large multitude of community-driven applications anchored on these pre-trained models. In light of this, our investigation revisits the fundamental causes, leading to our proposal of an innovative and principled remedy, called One More Step (OMS). By integrating a compact network and incorporating an additional simple yet effective step during inference, OMS elevates image fidelity and harmonizes the dichotomy between training and inference, while preserving original model parameters. Once trained, various pre-trained diffusion models with the same latent domain can share the same OMS module.Comment: Project Page: https://jabir-zheng.github.io/OneMoreStep/, Demo Page: https://huggingface.co/spaces/h1t/oms_sdxl_lc

PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning

In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that leveraging the textual space of a powerful pre-trained image-language model (such as CLIP) can be beneficial in learning visual features. Therefore, we develop a novel method termed PartSeg for few-shot part segmentation based on multimodal learning. Specifically, we design a part-aware prompt learning method to generate part-specific prompts that enable the CLIP model to better understand the concept of ``part'' and fully utilize its textual space. Furthermore, since the concept of the same part under different object categories is general, we establish relationships between these parts during the prompt learning process. We conduct extensive experiments on the PartImageNet and Pascal$\_$Part datasets, and the experimental results demonstrated that our proposed method achieves state-of-the-art performance

Unified Discrete Diffusion for Simultaneous Vision-Language Generation

The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In this work, we harness these traits and present a unified multimodal generation model that can conduct both the "modality translation" and "multi-modality generation" tasks using a single model, performing text-based, image-based, and even vision-language simultaneous generation. Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix. Moreover, we design a mutual attention module with fused embedding layer and a unified objective function to emphasise the inter-modal linkages, which are vital for multi-modality generation. Extensive experiments indicate that our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks

Genetic causal relationship between gut microbiome and psoriatic arthritis: a bidirectional two-sample Mendelian randomization study

BackgroundSeveral observational studies have suggested a potential relationship between gut microbiome and psoriatic arthritis (PsA). However, the causality of this relationship still remains unclear. We aim to explore if the specific gut microbiome is causally associated with PsA at the genetic level and offer valuable insights into the etiology of PsA.MethodsIn this study, we employed a bidirectional two-sample Mendelian randomization (MR) analysis to investigate the causal effects of the gut microbiome on PsA. Publicly accessible genome-wide association study summary data of gut microbiome were obtained from the MiBioGen consortium (n = 14,306), while the summary statistics of psoriatic arthropathies were sourced from the FinnGen consortium R8 release data (2,776 cases and 221,323 controls). The primary analytical method employed was inverse variance weighted (IVW), complemented by supplementary methods including MR-Egger, weighted median, weighted mode, maximum likelihood, MR-PRESSO, and cML-MA. Reverse MR analysis was performed on the bacteria that were found to be causally associated with PsA in forward MR analysis. Cochran’s IVW Q statistic was utilized to assess the heterogeneity of instrumental variables among the selected single nucleotide polymorphisms.ResultsIVW estimates revealed that Ruminococcaceae_UCG-002 (odds ratio (OR) = 0.792, 95% confidence interval (CI), 0.643–0.977, p = 0.029) exhibited a protective effect on PsA. Conversely, Blautia (OR = 1.362, 95% CI, 1.008–1.842, p = 0.044), Eubacterium_fissicatena_group (OR = 1.28, 95% CI, 1.075–1.524, p = 0.006), and Methanobrevibacter (OR = 1.31, 95% CI, 1.059–1.621, p = 0.013) showed a positive correlation with the risk of PsA. No significant heterogeneity, horizontal pleiotropy, or outliers were observed, and the results of the MR analysis remained unaffected by any single nucleotide polymorphisms. According to the results of reverse MR analysis, no significant causal effect of PsA was found on gut microbiome.ConclusionThis study establishes for the first time a causal relationship between the gut microbiome and PsA, providing potential valuable strategies for the prevention and treatment of PsA. Further randomized controlled trials are urgently warranted to support the targeted protective mechanisms of probiotics on PsA

Transcriptome profile of halofuginone resistant and sensitive strains of Eimeria tenella

The antiparasitic drug halofuginone is important for controlling apicomplexan parasites. However, the occurrence of halofuginone resistance is a major obstacle for it to the treatment of apicomplexan parasites. Current studies have identified the molecular marker and drug resistance mechanisms of halofuginone in Plasmodium falciparum. In this study, we tried to use transcriptomic data to explore resistance mechanisms of halofuginone in apicomplexan parasites of the genus Eimeria (Apicomplexa: Eimeriidae). After halofuginone treatment of E. tenella parasites, transcriptome analysis was performed using samples derived from both resistant and sensitive strains. In the sensitive group, DEGs associated with enzymes were significantly downregulated, whereas the DNA damaging process was upregulated after halofuginone treatment, revealing the mechanism of halofuginone-induced parasite death. In addition, 1,325 differentially expressed genes (DEGs) were detected between halofuginone resistant and sensitive strains, and the DEGs related to translation were significantly downregulated after halofuginone induction. Overall, our results provide a gene expression profile for further studies on the mechanism of halofuginone resistance in E. tenella

The reporting quality of randomized controlled trials in Chinese herbal medicine (CHM) formulas for diabetes based on the consort statement and its extension for CHM formulas

Background: This study aimed to assess the overall reporting quality of randomized controlled trials (RCTs) in Chinese herbal medicine (CHM) formulas for patients with diabetes, and to identify factors associated with better reporting quality.Methods: Four databases including PubMed, Embase, Cochrane Library and Web of Science were systematically searched from their inception to December 2022. The reporting quality was assessed based on the Consolidated Standards of Reporting Trials (CONSORT) statement and its CHM formula extension. The overall CONSORT and its CHM formula extension scores were calculated and expressed as proportions separately. We also analyzed the pre-specified study characteristics and performed exploratory regressions to determine their associations with the reporting quality.Results: Seventy-two RCTs were included. Overall reporting quality (mean adherence) were 53.56% and 45.71% on the CONSORT statement and its CHM formula extension, respectively. The strongest associations with reporting quality based on the CONSORT statement were multiple centers and larger author numbers. Compliance with the CHM formula extension, particularly regarding the disclosure of the targeted traditional Chinese medicine (TCM) pattern (s), was generally insufficient.Conclusion: The reporting quality of RCTs in CHM formulas for diabetes remains unsatisfactory, and the adherence to the CHM formula extension is even poorer. In order to ensure transparent and standardized reporting of RCTs, it is essential to advocate for or even mandate adherence of the CONSORT statement and its CHM formula extension when reporting trials in CHM formulas for diabetes by both authors and editors

Gut microbiome-based noninvasive diagnostic model to predict acute coronary syndromes

BackgroundPrevious studies have shown that alterations in the gut microbiota are closely associated with Acute Coronary Syndrome (ACS) development. However, the value of gut microbiota for early diagnosis of ACS remains understudied.MethodsWe recruited 66 volunteers, including 29 patients with a first diagnosis of ACS and 37 healthy volunteers during the same period, collected their fecal samples, and sequenced the V4 region of the 16S rRNA gene. Functional prediction of the microbiota was performed using PICRUSt2. Subsequently, we constructed a nomogram and corresponding webpage based on microbial markers to assist in the diagnosis of ACS. The diagnostic performance and usefulness of the model were analyzed using boostrap internal validation, calibration curves, and decision curve analysis (DCA).ResultsCompared to that of healthy controls, the diversity and composition of microbial community of patients with ACS was markedly abnormal. Potentially pathogenic genera such as Streptococcus and Acinetobacter were significantly increased in the ACS group, whereas certain SCFA-producing genera such as Blautia and Agathobacter were depleted. In addition, in the correlation analysis with clinical indicators, the microbiota was observed to be associated with the level of inflammation and severity of coronary atherosclerosis. Finally, a diagnostic model for ACS based on gut microbiota and clinical variables was developed with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.963 (95% CI: 0.925–1) and an AUC value of 0.948 (95% CI: 0.549–0.641) for bootstrap internal validation. The calibration curves of the model show good consistency between the actual and predicted probabilities. The DCA showed that the model had a high net clinical benefit for clinical applications.ConclusionOur study is the first to characterize the composition and function of the gut microbiota in patients with ACS and healthy populations in Southwest China and demonstrates the potential effect of the microbiota as a non-invasive marker for the early diagnosis of ACS

Effects of water stress on starch synthesis and accumulation of two rice cultivars at different growth stages

Rice is a water intensive crop and soil water conditions affect rice yield and quality. However, there is limited research on the starch synthesis and accumulation of rice under different soil water conditions at different growth stages. Thus, a pot experiment was conducted to explore the effects of IR72 (indica) and Nanjing (NJ) 9108 (japonica) rice cultivars under flood-irrigated treatment (CK, 0 kPa), light water stress treatment (L, -20 ± 5 kPa), moderate water stress treatment (M, -40 ± 5 kPa) and severe water stress treatment (S, -60 ± 5 kPa) on the starch synthesis and accumulation and rice yield at booting stage (T1), flowering stage (T2) and filling stage (T3), respectively. Under LT treatment, the total soluble sugar and sucrose contents of both cultivars decreased while the amylose and total starch contents increased. Starch synthesis-related enzyme activities and their peak activities at mid-late growth stage increased as well. However, applying MT and ST treatments produced the opposite effects. The 1000-grain weight of both cultivars increased under LT treatment while the seed setting rate increased only under LT3 treatment. Compared with CK, water stress at booting stage decreased grain yield. The principal component analysis (PCA) showed that LT3 got the highest comprehensive score while ST1 got lowest for both cultivars. Furthermore, the comprehensive score of both cultivars under the same water stress treatment followed the trend of T3 > T2 > T1, and NJ 9108 had a better drought-resistant ability than IR72. Compared with CK, the grain yield under LT3 increased by 11.59% for IR72 and 16.01% for NJ 9108, respectively. Overall, these results suggested that light water stress at filling stage could be an effective method to enhance starch synthesis-related enzyme activities, promote starch synthesis and accumulation and increase grain yield