HFGD: High-level Feature Guided Decoder for Semantic Segmentation

Existing pyramid-based upsamplers (e.g. SemanticFPN), although efficient, usually produce less accurate results compared to dilation-based models when using the same backbone. This is partially caused by the contaminated high-level features since they are fused and fine-tuned with noisy low-level features on limited data. To address this issue, we propose to use powerful pretrained high-level features as guidance (HFG) when learning to upsample the fine-grained low-level features. Specifically, the class tokens are trained along with only the high-level features from the backbone. These class tokens are reused by the upsampler for classification, guiding the upsampler features to more discriminative backbone features. One key design of the HFG is to protect the high-level features from being contaminated with proper stop-gradient operations so that the backbone does not update according to the gradient from the upsampler. To push the upper limit of HFG, we introduce an context augmentation encoder (CAE) that can efficiently and effectively operates on low-resolution high-level feature, resulting in improved representation and thus better guidance. We evaluate the proposed method on three benchmarks: Pascal Context, COCOStuff164k, and Cityscapes. Our method achieves state-of-the-art results among methods that do not use extra training data, demonstrating its effectiveness and generalization ability. The complete code will be releasedComment: Revised version, refactored presentation and added more experiment

Experimental and theoretical characterization of microbial bioanodes formed in pulp and paper mill effluent in electrochemically controlled conditions

Microbial bioanodes were formed in pulp and paper effluent on graphite plate electrodes under constant polarization at -0.3 V/SCE, without any addition of nutriment or substrate. The bioanodes were characterized in 3-electrode set-ups, in continuous mode, with hydraulic retention times from 6 to 48 h and inlet COD from 500 to 5200 mg/L. Current densities around 4 A/m2 were obtained and voltammetry curves indicated that 6 A/m2 could be reached at +0.1 V/SCE. A theoretical model was designed, which allowed the effects of HRT and COD to be distinguished in the complex experimental data obtained with concomitant variations of the two parameters. COD removal due to the electrochemical process was proportional to the hydraulic retention time and obeyed a Michaelis–Menten law with respect to the COD of the outlet flow, with a Michaelis constant KCOD of 400 mg/L. An inhibition effect occurred above inlet COD of around 3000 mg/L

Microyielding of Core-Shell Crystal Dendrites in a Bulk-metallic-glass Matrix Composite

In-situ synchrotron x-ray experiments have been used to follow the evolution of the diffraction peaks for crystalline dendrites embedded in a bulk metallic glass matrix subjected to a compressive loading-unloading cycle. We observe irreversible diffraction-peak splitting even though the load does not go beyond half of the bulk yield strength. The chemical analysis coupled with the transmission electron microscopy mapping suggests that the observed peak splitting originates from the chemical heterogeneity between the core (major peak) and the stiffer shell (minor peak) of the dendrites. A molecular dynamics model has been developed to compare the hkl-dependent microyielding of the bulk metallic-glass matrix composite. The complementary diffraction measurements and the simulation results suggest that the interface, as Maxwell damper, between the amorphous matrix and the (211) crystalline planes relax under prolonged load that causes a delay in the reload curve which ultimately catches up with the original path

Application of Herbal Traditional Chinese Medicine in the Treatment of Acute Kidney Injury

Acute kidney injury (AKI) is a clinical syndrome characterized by a rapid loss of renal function, which may further develop into chronic kidney damage (CKD) or even end-stage renal disease (ESRD). AKI is a global health problem associated with high morbidity and costly treatments, and there is no specific or effective strategy to treat AKI. In recent years, Traditional Chinese Medicine (TCM) has attracted more attention, with lines of evidence showing that application of TCM improved AKI, and the mechanisms of action for some TCMs have been well illustrated. However, reviews summarizing the progress in this field are still lacking. In this paper, we reviewed TCM preparations and TCM monomers in the treatment of AKI over the last 10 years, describing their renal protective effects and mechanisms of action, including alleviating inflammation, programmed cell death, necrosis, and reactive oxygen species. By focusing on the mechanisms of TCMs to improve renal function, we provide effective complementary evidence to promote the development of TCMs to treat AKI. Moreover, we also summarized TCMs with nephrotoxicity, which provides a more comprehensive understanding of TCMs in the treatment of AKI. This review may provide a theoretical basis for the clinical application of TCMs in the future

ANPL: Compiling Natural Programs with Interactive Decomposition

The advents of Large Language Models (LLMs) have shown promise in augmenting programming using natural interactions. However, while LLMs are proficient in compiling common usage patterns into a programming language, e.g., Python, it remains a challenge how to edit and debug an LLM-generated program. We introduce ANPL, a programming system that allows users to decompose user-specific tasks. In an ANPL program, a user can directly manipulate sketch, which specifies the data flow of the generated program. The user annotates the modules, or hole with natural language descriptions offloading the expensive task of generating functionalities to the LLM. Given an ANPL program, the ANPL compiler generates a cohesive Python program that implements the functionalities in hole, while respecting the dataflows specified in sketch. We deploy ANPL on the Abstraction and Reasoning Corpus (ARC), a set of unique tasks that are challenging for state-of-the-art AI systems, showing it outperforms baseline programming systems that (a) without the ability to decompose tasks interactively and (b) without the guarantee that the modules can be correctly composed together. We obtain a dataset consisting of 300/400 ARC tasks that were successfully decomposed and grounded in Python, providing valuable insights into how humans decompose programmatic tasks. See the dataset at https://iprc-dip.github.io/DARC

Effects of Salvianolic Acid B on Protein Expression in Human Umbilical Vein Endothelial Cells

Salvianolic acid B (Sal B), a pure water-soluble compound extracted from Radix Salviae miltiorrhizae, has been reported to possess potential cardioprotective efficacy. To identify proteins or pathways by which Sal B might exert its protective activities on the cardiovascular system, two-dimensional gel electrophoresis-based comparative proteomics was performed, and proteins altered in their expression level after Sal B treatment were identified by MALDI-TOF MS/MS. Human umbilical vein endothelial cells (HUVECs) were incubated at Sal B concentrations that can be reached in human plasma by pharmacological intervention. Results indicated that caldesmon, an actin-stabilizing protein, was downregulated in Sal B-exposed HUVECs. Proteins that showed increased expression levels upon Sal B treatment were vimentin, T-complex protein 1, protein disulfide isomerase, tropomyosin alpha, heat shock protein beta-1, UBX domain-containing protein 1, alpha enolase, and peroxiredoxin-2. Additionally, Sal B leads to increased phosphorylation of nucleophosmin in a dose-dependent manner and promotes proliferation of HUVECs. We found that Sal B exhibited a coordinated regulation of enzymes and proteins involved in cytoskeletal reorganization, oxidative stress, and cell growth. Our investigation would provide understanding to the endothelium protection information of Sal B