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Black-odorous urban water bodies and sediments pose a serious environmental problem. In this study, we conducted microcosm batch experiments to investigate the effect of remediation reagents (magnesium hydroxide and calcium nitrate) on native bacterial communities and their ecological functions in the black-odorous sediment of urban water. The dominant phyla (Proteobacteria, Actinobacteria, Chloroflexi, and Planctomycetes) and classes (Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria, Actinobacteria, Anaerolineae, and Planctomycetia) were determined under calcium nitrate and magnesium hydroxide treatments. Functional groups related to aerobic metabolism, including aerobic chemoheterotrophy, dark sulfide oxidation, and correlated dominant genera (Thiobacillus, Lysobacter, Gp16, and Gaiella) became more abundant under calcium nitrate treatment, whereas functional genes potentially involved in dissimilatory sulfate reduction became less abundant. The relative abundance of chloroplasts, fermentation, and correlated genera (Desulfomonile and unclassified Cyanobacteria) decreased under magnesium hydroxide treatment. Overall, these results indicated that calcium nitrate addition improved hypoxia-related reducing conditions in the sediment and promoted aerobic chemoheterotrophy.

Particle-based Variational Inference (ParVI) methods have been widely adopted in deep Bayesian inference tasks such as Bayesian neural networks or Gaussian Processes, owing to their efficiency in generating high-quality samples given the score of the target distribution. Typically, ParVI methods evolve a weighted-particle system by approximating the first-order Wasserstein gradient flow to reduce the dissimilarity between the particle system’s empirical distribution and the target distribution. Recent advancements in ParVI have explored sophisticated gradient flows to obtain refined particle systems with either accelerated position updates or dynamic weight adjustments. In this paper, we introduce the semi-Hamiltonian gradient flow on a novel Information–Fisher–Rao space, known as the SHIFR flow, and propose the first ParVI framework that possesses both accelerated position update and dynamical weight adjustment simultaneously, named the General Accelerated Dynamic-Weight Particle-based Variational Inference (GAD-PVI) framework. GAD-PVI is compatible with different dissimilarities between the empirical distribution and the target distribution, as well as different approximation approaches to gradient flow. Moreover, when the appropriate dissimilarity is selected, GAD-PVI is also suitable for obtaining high-quality samples even when analytical scores cannot be obtained. Experiments conducted under both the score-based tasks and sample-based tasks demonstrate the faster convergence and reduced approximation error of GAD-PVI methods over the state-of-the-art.

Background Neonatal necrotizing enterocolitis (NEC) is prevalent among preterm neonates and is associated with high morbidity and mortality. While surgical intervention remains essential for advanced NEC, postoperative neonates exhibit an elevated risk of brain injury. However, which surgery-related factors exacerbate neonatal brain damage remains insufficiently investigated. This study aimed to identify risk factors for brain injury in neonates with surgical NEC and establish a predictive model to facilitate early identification and intervention, which may ultimately decrease neurodevelopmental impairment rates. Methods This study analyzed 181 consecutive NEC surgical cases at our tertiary referral center (2017–2023). Brain injury was confirmed by cranial MRI. Primary analysis was used to compare groups via Student's t test for normally distributed data and the Mann‒Whitney U/χ 2 test for nonparametric variables. Significant predictors ( p  < 0.05) were incorporated into multivariate logistic regression, with model performance validated by receiver operating characteristic (ROC) analysis. Results Multivariate analysis revealed six independent risk factors (all p  < 0.05): lower gestational age (GA) (OR 0.85 [95% CI: 0.73–0.98]; p  = 0.028), higher procalcitonin (PCT) levels at surgery (OR 1.03 [95% CI: 1.01–1.06]; p  = 0.017), postoperative sepsis (OR 3.46 [95% CI: 1.36–8.76]; p  = 0.009), transmural necrosis with perforation (OR 3.03 [95% CI: 1.18–7.78]; p  = 0.021), longer diagnosis-to-surgery intervals (> 24 h) (OR 3.52 [95% CI: 1.23–10.08]; p  = 0.019), and retention of the necrotic bowel (OR 4.88 [95% CI: 1.50–15.91]; p  = 0.009). The ROC analysis revealed an area under the curve (AUC) of 0.86 for the prediction model. Conclusion The incidence of brain injury in neonates with surgical NEC is independently associated with elevated PCT levels at surgery, the presence of sepsis, and the occurrence of transmural necrosis with perforation. Conversely, increased GA, early surgical recognition and intervention, and complete resection of the necrotic bowel may serve as potential protective factors against brain injury. The predictive model constructed on the basis of these findings demonstrated strong discriminative ability and we propose immediate neuroprotective intervention when model-predicted risk exceeds the 53% threshold.

Purpose The treatment of Hirschsprung’s disease (HD) in infants with the robotic-assisted Swenson procedure has been rarely reported. In this investigation, we aimed to explore the safety and the efficacy of robotic-assisted Swenson procedure for the HD in infants. Methods From November 2022 to July 2023, 17 cases of HD were treated with the Da Vinci robotic Xi surgical system using a three-port approach. Preoperative, intraoperative, and postoperative data were collected and compared with 43 cases of HD treated with laparoscopy by the same lead surgeon. Results The robotic-assisted surgery (RAS) group included 17 infants, and the laparoscopic surgery (LS) group included 43 infants, with a median surgical age of 35 days for both groups. There were no statistically significant differences between the two groups in terms of surgical age, gender, preoperative weight, preoperative hospital stay, preoperative enema time, and incidence of preoperative enterocolitis. Estimated intraoperative blood loss and transfusion rate in the RAS group were both lower than in the LS group, with statistically significant differences. There were no statistically significant differences in early and midterm postoperative complications (anastomotic leaks, anastomotic strictures, enterocolitis, etc.) between the two groups. Conclusion This study demonstrates the efficacy and the safety of robotic-assisted Swenson procedure in infants.

PurposeExcess nickel (Ni) entering lakes can pose adverse effects on aquatic ecosystems and human health. This study aimed to reveal the spatiotemporal distribution, mobilization kinetics, and potential risk of Ni in sediments of a typical multi-ecological lake, Lake Taihu, China.MethodsWe conducted seasonal monitoring of the spatial distribution of soluble and labile Ni in sediments using high-resolution dialysis samplers (HR-Peeper) and the diffusive gradient in thin-films technique (DGT), respectively.ResultsWe found that the total Ni concentrations in sediments (mean: 37.56 mg kg−1) all exceeded the background value (19.5 mg kg−1). The spatial distributions of soluble and labile Ni showed no notable fluctuations along the vertical profiles of sediments in all seasons. The DGT-induced fluxes model implied that there is a partial Ni resupply capacity in the sediment of all three ecological zones, but it is higher in the algal-type zones than in the macrophyte-type and transition zones. Furthermore, an assessment of the ecotoxicological risk found that the risk quotient values for Ni were less than 1 in all sampling seasons, indicating a low ecotoxicological risk of Ni in the sediments of Lake Taihu.ConclusionOur results indicate that the ecological risk in the algal-type lake zones requires special attention. Our findings help towards improving the level of understanding regarding the mobilization process and potential risk of Ni in sediments, which in turn can provide guidance for the prevention and control of sediment Ni pollution in lakes with multiple types of ecological zones.

In this paper, we propose a novel image process scheme called class-based expansion learning for image classification, which aims at improving the supervision-stimulation frequency for the samples of the confusing classes. Class-based expansion learning takes a bottom-up growing strategy in a class-based expansion optimization fashion, which pays more attention to the quality of learning the fine-grained classification boundaries for the preferentially selected classes. Besides, we develop a class confusion criterion to select the confusing class preferentially for training. In this way, the classification boundaries of the confusing classes are frequently stimulated, resulting in a fine-grained form. Experimental results demonstrate the effectiveness of the proposed scheme on several benchmarks.

As a challenging task, text-to-image generation aims to generate photo-realistic and semantically consistent images according to the given text descriptions. Existing methods mainly extract the text information from only one sentence to represent an image and the text representation effects the quality of the generated image well. However, directly utilizing the limited information in one sentence misses some key attribute descriptions, which are the crucial factors to describe an image accurately. To alleviate the above problem, we propose an effective text representation method with the complements of attribute information. Firstly, we construct an attribute memory to jointly control the text-to-image generation with sentence input. Secondly, we explore two update mechanisms, sample-aware and sample-joint mechanisms, to dynamically optimize a generalized attribute memory. Furthermore, we design an attribute-sentence-joint conditional generator learning scheme to align the feature embeddings among multiple representations, which promotes the cross-modal network training. Experimental results illustrate that the proposed method obtains substantial performance improvements on both the CUB (FID from 14.81 to 8.57) and COCO (FID from 21.42 to 12.39) datasets.

Continual Learning (CL) aims to learn in non-stationary scenarios, progressively acquiring and maintaining knowledge from sequential tasks. Recent Prompt-based Continual Learning (PCL) has achieved remarkable performance with Pre-Trained Models (PTMs). These approaches grow a prompt sets pool by adding a new set of prompts when learning each new task (\\emph{prompt learning}) and adopt a matching mechanism to select the correct set for each testing sample (\\emph{prompt retrieval}). Previous studies focus on the latter stage by improving the matching mechanism to enhance Prompt Retrieval Accuracy (PRA). To promote cross-task knowledge facilitation and form an effective and efficient prompt sets pool, we propose a plug-in module in the former stage to \\textbf{Learn Whether to Grow (LW2G)} based on the disparities between tasks. Specifically, a shared set of prompts is utilized when several tasks share certain commonalities, and a new set is added when there are significant differences between the new task and previous tasks. Inspired by Gradient Projection Continual Learning, our LW2G develops a metric called Hinder Forward Capability (HFC) to measure the hindrance imposed on learning new tasks by surgically modifying the original gradient onto the orthogonal complement of the old feature space. With HFC, an automated scheme Dynamic Growing Approach adaptively learns whether to grow with a dynamic threshold. Furthermore, we design a gradient-based constraint to ensure the consistency between the updating prompts and pre-trained knowledge, and a prompts weights reusing strategy to enhance forward transfer. Extensive experiments show the effectiveness of our method. The source codes are available at \\url{https://github.com/RAIAN08/LW2G}.

Incremental unlearning (IU) is critical for pre-trained models to comply with sequential data deletion requests, yet existing methods primarily suppress parameters or confuse knowledge without explicit constraints on both feature and gradient level, resulting in \\textit{superficial forgetting} where residual information remains recoverable. This incomplete forgetting risks security breaches and disrupts retention balance, especially in IU scenarios. We propose FG-OrIU (\\textbf{F}eature-\\textbf{G}radient \\textbf{Or}thogonality for \\textbf{I}ncremental \\textbf{U}nlearning), the first framework unifying orthogonal constraints on both features and gradients level to achieve deep forgetting, where the forgetting effect is irreversible. FG-OrIU decomposes feature spaces via Singular Value Decomposition (SVD), separating forgetting and remaining class features into distinct subspaces. It then enforces dual constraints: feature orthogonal projection on both forgetting and remaining classes, while gradient orthogonal projection prevents the reintroduction of forgotten knowledge and disruption to remaining classes during updates. Additionally, dynamic subspace adaptation merges newly forgetting subspaces and contracts remaining subspaces, ensuring a stable balance between removal and retention across sequential unlearning tasks. Extensive experiments demonstrate the effectiveness of our method.

The success of diffusion models has raised concerns about the generation of unsafe or harmful content, prompting concept erasure approaches that fine-tune modules to suppress specific concepts while preserving general generative capabilities. However, as the number of erased concepts grows, these methods often become inefficient and ineffective, since each concept requires a separate set of fine-tuned parameters and may degrade the overall generation quality. In this work, we propose a supertype-subtype concept hierarchy that organizes erased concepts into a parent-child structure. Each erased concept is treated as a child node, and semantically related concepts (e.g., macaw, and bald eagle) are grouped under a shared parent node, referred to as a supertype concept (e.g., bird). Rather than erasing concepts individually, we introduce an effective and efficient group-wise suppression method, where semantically similar concepts are grouped and erased jointly by sharing a single set of learnable parameters. During the erasure phase, standard diffusion regularization is applied to preserve denoising process in unmasked regions. To mitigate the degradation of supertype generation caused by excessive erasure of semantically related subtypes, we propose a novel method called Supertype-Preserving Low-Rank Adaptation (SuPLoRA), which encodes the supertype concept information in the frozen down-projection matrix and updates only the up-projection matrix during erasure. Theoretical analysis demonstrates the effectiveness of SuPLoRA in mitigating generation performance degradation. We construct a more challenging benchmark that requires simultaneous erasure of concepts across diverse domains, including celebrities, objects, and pornographic content.