BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout

Using synthesized images to boost the performance of perception models is a long-standing research challenge in computer vision. It becomes more eminent in visual-centric autonomous driving systems with multi-view cameras as some long-tail scenarios can never be collected. Guided by the BEV segmentation layouts, the existing generative networks seem to synthesize photo-realistic street-view images when evaluated solely on scene-level metrics. However, once zoom-in, they usually fail to produce accurate foreground and background details such as heading. To this end, we propose a two-stage generative method, dubbed BEVControl, that can generate accurate foreground and background contents. In contrast to segmentation-like input, it also supports sketch style input, which is more flexible for humans to edit. In addition, we propose a comprehensive multi-level evaluation protocol to fairly compare the quality of the generated scene, foreground object, and background geometry. Our extensive experiments show that our BEVControl surpasses the state-of-the-art method, BEVGen, by a significant margin, from 5.89 to 26.80 on foreground segmentation mIoU. In addition, we show that using images generated by BEVControl to train the downstream perception model, it achieves on average 1.29 improvement in NDS score.Comment: 13 pages, 8 figure

Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation

Using generative models to synthesize new data has become a de-facto standard in autonomous driving to address the data scarcity issue. Though existing approaches are able to boost perception models, we discover that these approaches fail to improve the performance of planning of end-to-end autonomous driving models as the generated videos are usually less than 8 frames and the spatial and temporal inconsistencies are not negligible. To this end, we propose Delphi, a novel diffusion-based long video generation method with a shared noise modeling mechanism across the multi-views to increase spatial consistency, and a feature-aligned module to achieves both precise controllability and temporal consistency. Our method can generate up to 40 frames of video without loss of consistency which is about 5 times longer compared with state-of-the-art methods. Instead of randomly generating new data, we further design a sampling policy to let Delphi generate new data that are similar to those failure cases to improve the sample efficiency. This is achieved by building a failure-case driven framework with the help of pre-trained visual language models. Our extensive experiment demonstrates that our Delphi generates a higher quality of long videos surpassing previous state-of-the-art methods. Consequentially, with only generating 4% of the training dataset size, our framework is able to go beyond perception and prediction tasks, for the first time to the best of our knowledge, boost the planning performance of the end-to-end autonomous driving model by a margin of 25%.Comment: Project Page: https://westlake-autolab.github.io/delphi.github.io/, 8 figure

Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy

ObjectiveTo establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy.MethodsA total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis.ResultsHistopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91).ConclusionWe established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy

A sticky carbohydrate meets a mussel adhesive: Catechol-conjugated levan for hemostatic and wound healing applications

The stickiest natural polysaccharide, levan, plays a role in metalloproteinase activation, which is an important step involved in the healing of injured tissue. However, levan is easily diluted, washed away, and loses adhesion in wet environments, which limits its biomedical applications. Herein, we demonstrate a strategy for fabricating a levan-based adhesive hydrogel for hemostatic and tissue adhesion applications by conjugating catechol to levan. Prepared hydrogels exhibit significantly improved water solubilities, and adhesion strengths to hydrated porcine skin of up to 42.17 ± 0.24 kPa which is more than three-times that of fibrin glue adhesive. The hydrogels also promote rapid blood clotting and significantly faster healing of rat-skin incisions compared to nontreated samples. In addition, levan-catechol exhibited an immune response close to that of the negative control, which is ascribable to its significantly lower endotoxin level compared to native levan. Overall, levan-catechol hydrogels are promising materials for hemostatic and tissue-adhesion applications.11Nsciescopu

Evaluation of spatial distribution of carbon emissions from land use and environmental parameters: A case study in the Yangtze River Delta demonstration zone

In order to attain sustainable carbon management and reduce greenhouse gas emissions, it is imperative to obtain comprehensive data on estimated carbon emissions. This study presents an accounting framework that incorporates environmental parameters in land use to account for the spatial heterogeneity of carbon emissions. In the framework, carbon emissions were estimated based on direct and indirect emissions from where their emissions were generated. Environmental parameters are incorporated as spatial variables in the process of estimating carbon emissions from land use to obtain spatially explicit carbon emission information. In this study, the Demonstration Zone of Green and Integrated Ecological Development of the Yangtze River Delta (demonstration area) is taken as a case study to verify the applicability of the framework. The findings clearly show the spatial association between the spatial distribution of carbon emissions and land use-related carbon emissions. The study outcomes are valuable in facilitating the demonstration area's attainment of sustainable development objectives. The case study can also provide insights into the Yangtze River Delta's carbon emission management and sustainable development practices

The mediating effect of depression on the association between lung disease and cardiovascular health

Introduction In this study, we investigated the effect of depression on the interaction between lung disease and cardiovascular health (CVH). Methods Utilising data from the National Health and Nutrition Examination Survey (2013–2018), we employed multivariate regression and bootstrap mediation analysis to explore the relationships among lung diseases, depression, and CVH scores. Results Complex and significant associations were identified among lung diseases, depression, and CVH scores, with depression mediating 9.42% of the effect on CVH, especially for chronic bronchitis patients. Conclusions Depression significantly mediated the relationship between lung disease and reduced CVH scores, highlighting the importance of mental health management in lung disease patients

Photocatalytic oxidation of cyclohexane on ultra-fine TiO₂ particles

785-788Photocatalytic oxidation of cyclohexane has been investigated on nanometer size TiO2 particles under mild condition. Photocatalytic activity increases with decrease in particle size and depends on the type and the microstructure of crystallites. The selectivity of the product (cyclohexanol) is very high (≥85%). The results suggest that a size quantization effect is operating and reduction of size might result in some electronic modification of TiO2 to produce an enhancement of the activities of electrons and holes

Altered myelination in the Niemann-Pick type C1 mutant mouse

Niemann–Pick type C1 (NPC1) disease is a lysosomal storage disorder caused by mutation of Npc1 or Npc2 gene, resulting in various progressive pathological features. Myelin defection is a major pathological problem in Npc1 mutant mice; however, impairment of myelin proteins in the developing brain is still incompletely understood. In this study, we showed that the expression of myelin genes and proteins is strongly inhibited from postnatal day 35 onwards including reduced myelin basic protein (MBP) expression in the brain. Furthermore, myelination characterized by MBP immunohistochemistry was strongly perturbed in the forebrain, moderately in the midbrain and cerebellum, and slightly in the hindbrain. Our results demonstrate that mutation of the Npc1 gene is sufficient to cause severe and progressive defects in myelination in the mouse brain

Preventive effect of swim bladder hydrolysates on cyclophosphamide-induced ovarian injury in mice

Aims This study aimed to prepare swim bladder hydrolysate (SBH) with M-n < 4000 Da, and investigate its effects on cyclophosphamide (CTX)-mediated ovarian injury in mice. Methods Hydrolysates were prepared by heating extraction, enzymatic hydrolysis and ultrafiltration. M-n and distribution of SBH were analyzed via gel filtration chromatography and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Changes in the mouse oestrus cycle were determined by cytological examination. The number of follicles was examined using histopathology. Enzyme-linked immunosorbent assays (ELISAs) were used to determine the serum sex hormone levels. Results The M-n of SBH, prepared by heating extraction, enzymatic hydrolysis, ultrafiltration, and from different batches, was below 4000 Da, and the preparation process was stable. Compared with the control group, the low-, middle-, and high-dose SBH treatment groups showed different trends in oestrus duration, serum sex hormone levels, and the number of primordial and secondary follicles. The oestrus cycle duration of the high-dose SBH group was longer than that of the model group. The serum luteinizing hormone, follicle-stimulating hormone, and anti-Mullerian hormone levels in the middle-dose group were the closest to those of control group. The number of primordial and secondary follicles in the medium-dose group was significantly higher than that in the model group and closest to those of control group. Conclusion After heating extraction, trypsin/Flavourzyme hydrolysis and ultrafiltration, a hydrolysate with M-n below 4000 Da could be prepared. We found that a moderate (400 mg/kg) SBH dose resulted in the greatest effect on ovarian injury remission in mice