Bilateral Network with Residual U-blocks and Dual-Guided Attention for Real-time Semantic Segmentation

When some application scenarios need to use semantic segmentation technology, like automatic driving, the primary concern comes to real-time performance rather than extremely high segmentation accuracy. To achieve a good trade-off between speed and accuracy, two-branch architecture has been proposed in recent years. It treats spatial information and semantics information separately which allows the model to be composed of two networks both not heavy. However, the process of fusing features with two different scales becomes a performance bottleneck for many nowaday two-branch models. In this research, we design a new fusion mechanism for two-branch architecture which is guided by attention computation. To be precise, we use the Dual-Guided Attention (DGA) module we proposed to replace some multi-scale transformations with the calculation of attention which means we only use several attention layers of near linear complexity to achieve performance comparable to frequently-used multi-layer fusion. To ensure that our module can be effective, we use Residual U-blocks (RSU) to build one of the two branches in our networks which aims to obtain better multi-scale features. Extensive experiments on Cityscapes and CamVid dataset show the effectiveness of our method

Single and combined use of the platelet-lymphocyte ratio, neutrophil-lymphocyte ratio, and systemic immune-inflammation index in gastric cancer diagnosis

IntroductionThe platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII) are markers for systemic inflammatory responses and have been shown by numerous studies to correlate with the prognosis of gastric cancer (GC). However, the diagnostic value of these three markers in GC is unclear, and no research has examined them in combination. In this study, we investigated the value of the PLR, NLR, and SII individually or in combination for GC diagnosis and elucidated the connection of these three markers with GC patients’ clinicopathological features.MethodsThis retrospective study was conducted on 125 patients diagnosed with GC and 125 healthy individuals, whose peripheral blood samples were obtained for analysis. The preoperative PLR, NLR, and SII values were subsequently calculated.ResultsThe results suggest that the PLR, NLR, and SII values of the GC group were considerably higher than those of the healthy group (all P ≤ 0.001); moreover, all three parameters were notably higher in early GC patients (stage I/II) than in the healthy population. The diagnostic value of each index for GC was analyzed using receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) calculation. The diagnostic efficacy of the SII alone (AUC: 0.831; 95% confidence interval [CI], 0.777–0.885) was expressively better than those of the NLR (AUC: 0.821; 95% CI: 0.769–0.873, P = 0.017) and PLR (AUC: 0.783; 95% CI: 0.726–0.840; P = 0.020). The AUC value of the combination of the PLR, NLR, and SII (AUC: 0.843; 95% CI: 0.791–0.885) was significantly higher than that of the combination of the SII and NLR (0.837, 95% CI: 0.785–0.880, P≤0.05), PLR (P = 0.020), NLR (P = 0.017), or SII alone (P ≤ 0.001). The optimal cut-off values were determined for the PLR, NLR, and SII using ROC analysis (SII: 438.7; NLR: 2.1; PLR: 139.5). Additionally, the PLR, NLR, and SII values were all meaningfully connected with the tumor size, TNM stage, lymph node metastasis, and serosa invasion (all P ≤ 0.05). Elevated levels of the NLR and SII were linked to distant metastasis (all P ≤ 0.001).DiscussionThese data suggest that the preoperative PLR, NLR, and SII could thus be utilized as diagnostic markers for GC or even early GC. Among these three indicators, the SII had the best diagnostic efficacy for GC, and the combination of the three could further improve diagnostic efficiency

Highly Uniform and Porous Polyurea Microspheres: Clean and Easy Preparation by Interface Polymerization, Palladium Incorporation, and High Catalytic Performance for Dye Degradation

Owing to their high specific surface area and low density, porous polymer materials are of great importance in a vast variety of applications, particularly as supports for enzymes and transition metals. Herein, highly uniform and porous polyurea microspheres (PPM), with size between 200 and 500 μm, are prepared by interfacial polymerization of toluene diisocyanate (TDI) in water through a simple microfluidic device composed of two tube lines, in one of which TDI is flowing and merged to the other with flowing aqueous phase, generating therefore TDI droplets at merging. The polymerization starts in the tube while flowing to the reactor and completed therein. This is a simple, easy and effective process for preparation of uniform PPM. Results demonstrate that the presence of polyvinyl alcohol in the aqueous flow is necessary to obtain uniform PPM. The size of PPM is readily adjustable by changing the polymerization conditions. In addition, palladium is incorporated in PPM to get the composite microspheres Pd@PPM, which are used as catalyst in degradation of methylene blue and rhodamine B. High performance and good reusability are demonstrated. Monodispersity, efficient dye degradation, easy recovery, and remarkable reusability make Pd@PPM a promising catalyst for dye degradation

Modification of m5C regulators in sarcoma can guide different immune infiltrations as well as immunotherapy

BackgroundRecent studies have found that 5-methylcytosine (m5C) modulators are associated with the prognosis and treatment of cancer. However, the relevance of m5C modulators in sarcoma prognosis and the tumour microenvironment is unclear.MethodsWe selected 15 m5C regulators and performed unsupervised clustering to identify m5C modification patterns and differentially expressed genes associated with the m5C phenotype in The Cancer Genome Atlas (TCGA) sarcomas. The extent of immune cell infiltration in different clustering groups was explored using single-sample gene set enrichment analysis and estimation algorithms. A principal component analysis algorithm-based m5C scoring protocol was performed to assess the m5C modification patterns of individual tumors.ResultsWe identified two distinct m5C modification patterns in the TCGA sarcoma cohort, which possess different clinical outcomes and biological processes. Tumour microenvironment analysis revealed two groups of immune infiltration patterns highly consistent with m5C modification patterns, classified as immune inflammatory and immune desert types. We constructed m5C scores and found that high m5C scores were closely associated with leiomyosarcoma and other subtypes, and were associated with poorer prognosis, lower PD-L1 expression, and poorer immunotherapy outcomes. The best application was validated against the m5C database.ConclusionWe constructed an m5C score for sarcoma based on the TCGA database and identified a poorer prognosis in the high m5c score group. The stability and good prognostic predictive power of the m5C score was verified by an external database. We found that sarcomas in the low m5C score group may have a better response to immunotherapy

MobileInst: Video Instance Segmentation on the Mobile

Video instance segmentation on mobile devices is an important yet very challenging edge AI problem. It mainly suffers from (1) heavy computation and memory costs for frame-by-frame pixel-level instance perception and (2) complicated heuristics for tracking objects. To address those issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices. Firstly, MobileInst adopts a mobile vision transformer to extract multi-level semantic features and presents an efficient query-based dual-transformer instance decoder for mask kernels and a semantic-enhanced mask decoder to generate instance segmentation per frame. Secondly, MobileInst exploits simple yet effective kernel reuse and kernel association to track objects for video instance segmentation. Further, we propose temporal query passing to enhance the tracking ability for kernels. We conduct experiments on COCO and YouTube-VIS datasets to demonstrate the superiority of MobileInst and evaluate the inference latency on one single CPU core of Snapdragon 778G Mobile Platform, without other methods of acceleration. On the COCO dataset, MobileInst achieves 31.2 mask AP and 433 ms on the mobile CPU, which reduces the latency by 50% compared to the previous SOTA. For video instance segmentation, MobileInst achieves 35.0 AP on YouTube-VIS 2019 and 30.1 AP on YouTube-VIS 2021. Code will be available to facilitate real-world applications and future research.Comment: Accepted by AAAI 2024 Main Track; Code will be release