PFS-b-PNIPAM:A first step towards polymeric nanofibrillar hydrogels based on uniform fiber-like micelles

Amphiphilic crystalline-coil diblock copolymers polyferrocenyldimethylsilane-<i>block</i>-poly­(<i>N</i>-isopropylacrylamide) of two different block ratios (PFS₅₆-<i>b</i>-PNIPAM₁₉₀ and PFS₂₆-<i>b</i>-PNIPAM₅₂₀) were synthesized by a copper-catalyzed azide–alkyne coupling reaction. They exhibited pronounced differences in self-assembly in alcohol solvents. While PFS₅₆-<i>b</i>-PNIPAM₁₉₀ formed mixtures of spherical and rod-like micelles in ethanol and 2-propanol, PFS₂₆-<i>b</i>-PNIPAM₅₂₀ formed long fibers of uniform width in these solvents. We used a seeded growth protocol to grow rod-like PFS₂₆-<i>b</i>-PNIPAM₅₂₀ micelles of uniform lengths. There were two surprising features of this experiment: First, micelle growth was unusually slow and required a long aging time (40 days) for them to reach their final length. Second, the micelles were characterized by a low number of polymer chains per unit length as determined by multiangle light scattering. This result suggests a loose packing of PFS chains in the micelle core. In an attempt to prepare thermoresponsive nanofibrillar hydrogels from these micelles, we explored approaches to transfer them from 2-propanol to water. These attempts were accompanied by extensive fragmentation of the micelles. We believe the fragility of these micelles is related to the loosely packed nature of the PFS chains in the micelle core. Fragmentation may also be affected by the cononsolvency effect of 2-propanol-water mixtures on the PNIPAM corona of the micelles. We could show, however, that the micelle fragments in water retained their anticipated thermoresponsive behavior

A Survey on Deep Clustering: From the Prior Perspective

Facilitated by the powerful feature extraction ability of neural networks, deep clustering has achieved great success in analyzing high-dimensional and complex real-world data. The performance of deep clustering methods is affected by various factors such as network structures and learning objectives. However, as pointed out in this survey, the essence of deep clustering lies in the incorporation and utilization of prior knowledge, which is largely ignored by existing works. From pioneering deep clustering methods based on data structure assumptions to recent contrastive clustering methods based on data augmentation invariances, the development of deep clustering intrinsically corresponds to the evolution of prior knowledge. In this survey, we provide a comprehensive review of deep clustering methods by categorizing them into six types of prior knowledge. We find that in general the prior innovation follows two trends, namely, i) from mining to constructing, and ii) from internal to external. Besides, we provide a benchmark on five widely-used datasets and analyze the performance of methods with diverse priors. By providing a novel prior knowledge perspective, we hope this survey could provide some novel insights and inspire future research in the deep clustering community

Decoupled Contrastive Multi-view Clustering with High-order Random Walks

In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i.e., some intra-cluster samples are wrongly treated as negative pairs. Although promising performance has been achieved by these methods, the false negative issue is still far from addressed and the false positive issue emerges because all in- and out-of-neighborhood samples are simply treated as positive and negative, respectively. To address the issues, we propose a novel robust method, dubbed decoupled contrastive multi-view clustering with high-order random walks (DIVIDE). In brief, DIVIDE leverages random walks to progressively identify data pairs in a global instead of local manner. As a result, DIVIDE could identify in-neighborhood negatives and out-of-neighborhood positives. Moreover, DIVIDE embraces a novel MvC architecture to perform inter- and intra-view contrastive learning in different embedding spaces, thus boosting clustering performance and embracing the robustness against missing views. To verify the efficacy of DIVIDE, we carry out extensive experiments on four benchmark datasets comparing with nine state-of-the-art MvC methods in both complete and incomplete MvC settings

EffLiFe: Efficient Light Field Generation via Hierarchical Sparse Gradient Descent

With the rise of Extended Reality (XR) technology, there is a growing need for real-time light field generation from sparse view inputs. Existing methods can be classified into offline techniques, which can generate high-quality novel views but at the cost of long inference/training time, and online methods, which either lack generalizability or produce unsatisfactory results. However, we have observed that the intrinsic sparse manifold of Multi-plane Images (MPI) enables a significant acceleration of light field generation while maintaining rendering quality. Based on this insight, we introduce EffLiFe, a novel light field optimization method, which leverages the proposed Hierarchical Sparse Gradient Descent (HSGD) to produce high-quality light fields from sparse view images in real time. Technically, the coarse MPI of a scene is first generated using a 3D CNN, and it is further sparsely optimized by focusing only on important MPI gradients in a few iterations. Nevertheless, relying solely on optimization can lead to artifacts at occlusion boundaries. Therefore, we propose an occlusion-aware iterative refinement module that removes visual artifacts in occluded regions by iteratively filtering the input. Extensive experiments demonstrate that our method achieves comparable visual quality while being 100x faster on average than state-of-the-art offline methods and delivering better performance (about 2 dB higher in PSNR) compared to other online approaches.Comment: Submitted to IEEE TPAM

Effects of self-healing biomimetic subsoiler on tillage resistance, wear-corrosion performance and soil disturbance morphology under different soil types

Subsoiling has been widely used all over the world as an important operation method of no-tillage farming. For energy-saving and life-extension, the tillage resistance and wear-corrosion of subsoilers have attracted wide attention. In this study, the tillage resistance, soil disturbance, wear and corrosion of subsoiler with S-T-SK-2# biomimetic structures (S means subsoiler; T means tine; SK means shank; 2#, h/s=0.57, h=5 mm and α=45°.) and self-healing coating under two seasons, two locations with different soil properties (black loam and clay soil) and subsoiling speeds (2 km/h and 3.6 km/h) were investigated. The soil moisture content and compactness affected the tillage resistance and wear-corrosion. The tillage resistance and degree of corrosion on all subsoilers were much larger in clay soil than that in black loam soil. Compared with S-T-SK-2#, the tillage reduction rate of C-S-T-SK-2# (S-T-SK-2# with self-healing coating) was up to 14.32% in clay soil under the speed of 2 km/h. The significance tests of regression equation results showed that subsoiler type and soil properties had a significant impact on soil disturbance coefficient, swelling of total soil layer, bulkiness of the plough pan. It is of a guiding significance for the analysis of soil disturbance. Synergism mechanism of subsoiler coupling with biomimetic structures and self-healing coating was analyzed in following. It depicted the guiding effect of biomimetic structure and the shield function of self-healing coating, resulting in anticorrosion and wear resistance of subsoiler

Autonomous self-evolving research on biomedical data: the DREAM paradigm

In contemporary biomedical research, the efficiency of data-driven approaches is hindered by large data volumes, tool selection complexity, and human resource limitations, necessitating the development of fully autonomous research systems to meet complex analytical needs. Such a system should include the ability to autonomously generate research questions, write analytical code, configure the computational environment, judge and interpret the results, and iteratively generate in-depth questions or solutions, all without human intervention. Here we developed DREAM, the first biomedical Data-dRiven self-Evolving Autonomous systeM, which can independently conduct scientific research without human involvement. Utilizing a clinical dataset and two omics datasets, DREAM demonstrated its ability to raise and deepen scientific questions, with difficulty scores for clinical data questions surpassing top published articles by 5.7% and outperforming GPT-4 and bioinformatics graduate students by 58.6% and 56.0%, respectively. Overall, DREAM has a success rate of 80% in autonomous clinical data mining. Certainly, human can participate in different steps of DREAM to achieve more personalized goals. After evolution, 10% of the questions exceeded the average scores of top published article questions on originality and complexity. In the autonomous environment configuration of the eight bioinformatics workflows, DREAM exhibited an 88% success rate, whereas GPT-4 failed to configure any workflows. In clinical dataset, DREAM was over 10,000 times more efficient than the average scientist with a single computer core, and capable of revealing new discoveries. As a self-evolving autonomous research system, DREAM provides an efficient and reliable solution for future biomedical research. This paradigm may also have a revolutionary impact on other data-driven scientific research fields.11 pages, 4 figures, content added, typos in figure corrected, references revised and font change

MoWE: Mixture of Weather Experts for Multiple Adverse Weather Removal

Currently, most adverse weather removal tasks are handled independently, such as deraining, desnowing, and dehazing. However, in autonomous driving scenarios, the type, intensity, and mixing degree of the weather are unknown, so the separated task setting cannot deal with these complex conditions well. Besides, the vision applications in autonomous driving often aim at high-level tasks, but existing weather removal methods neglect the connection between performance on perceptual tasks and signal fidelity. To this end, in upstream task, we propose a novel \textbf{Mixture of Weather Experts(MoWE)} Transformer framework to handle complex weather removal in a perception-aware fashion. We design a \textbf{Weather-aware Router} to make the experts targeted more relevant to weather types while without the need for weather type labels during inference. To handle diverse weather conditions, we propose \textbf{Multi-scale Experts} to fuse information among neighbor tokens. In downstream task, we propose a \textbf{Label-free Perception-aware Metric} to measure whether the outputs of image processing models are suitable for high level perception tasks without the demand for semantic labels. We collect a syntactic dataset \textbf{MAW-Sim} towards autonomous driving scenarios to benchmark the multiple weather removal performance of existing methods. Our MoWE achieves SOTA performance in upstream task on the proposed dataset and two public datasets, i.e. All-Weather and Rain/Fog-Cityscapes, and also have better perceptual results in downstream segmentation task compared to other methods. Our codes and datasets will be released after acceptance

Comparison of pedicle screw fixation by four different posterior approaches for the treatment of type A thoracolumbar fractures without neurologic injury

PurposeThis study was designed to compare the pedicle screw fixation by four different posterior approaches for the treatment of type A thoracolumbar fractures without neurologic injury.MethodsA total of 165 patients with type A thoracolumbar fractures without neurologic injury who received pedicle screw fixation by posterior approaches from February 2017 to August 2018 were enrolled in this study. They were further divided into the following four groups according to different posterior approaches: Open-C group (conventional open approach), Open-W group (Wiltse approach), MIS-F group (percutaneous approach with fluoroscopy guidance), and MIS-O group (percutaneous approach with O-arm navigation). The demographic data, clinical outcomes, and radiologic parameters were evaluated and compared among the four groups.ResultsThere were no significant differences in age, gender, fracture segment, and follow-up time. The incision length, blood loss, hospital stay time, and VAS (Visual Analog Scale) and ODI (Oswestry Disability Index) scores at the early stage of post-operation were the worst in the Open-C group. The MIS-O group showed significantly higher accuracy rate of pedicle position than other groups. The preoperative and postoperative AVH (anterior vertebral height) and VWA (vertebral wedge angle) obtain obvious correction in all patients immediately after and 1 year post-operation. No difference was found among the four groups at the final radiographic follow-up.ConclusionsThe four different posterior approaches are effective in treating type A thoracolumbar fractures in our study. Each approach has its own individual strengths and weaknesses and therefore requires comprehensive consideration prior to use. Proper approaches selection is critical to patients

A 7000-year record of environmental change: Evolution of Holocene environment and human activities in the Hangjiahu Plain, the lower Yangtze, China

The Hangjiahu Plain in the lower Yangtze is one of the core areas that sustained the flourishment of the Liangzhu Civilization. This study reconstructed Holocene environmental change on the Hangjiahu Plain based on a sediment core collected from the Tangqi ZK-3 location situated on the low-lying Hangzhou-Taihu region of the Yangtze Delta. We applied OSL dating, grain size analysis, pollen analysis, and magnetic susceptibility to reconstruct Holocene environmental change and compared our data with other published results. Our results showed that (i) before ~7.0 ka B.P., the ZK-3 core recorded a strong hydrodynamic force, resulting in the widespread deposition of light grayish silt clay or clayey silt in the region. The climate was warm and humid, and the vegetation was mixed evergreen deciduous coniferous forest. (ii) Between 7.0 and 6.0 ka B.P., the hydrodynamic condition in ZK-3 core became weaker, and the climate remained warm and humid. Although most of the Hangjiahu Plain were still covered by the light grayish silt clay or clayey silt, some higher grounds began to emerge as sea-level rise slowed, which coincided with the development of the Majiabang culture. (iii) Between 6.0 and 4.5 ka B.P., the deposition of yellowish silty clay indicates a shallow-water hydrological environment at ZK-3, as the regional water level was dropping while more land was emerging, which provided a favorable physical environment for the prosperity of the Songze and Liangzhu cultures. The period experienced a drier and cooler climate, with evidence of deforestation. (iv) Between 4.5 and 3.0 ka B.P., the sediments in the ZK-3 core were dominated by light grayish clay, indicative of a return to a deep-water environment with a prolonged waterlogging condition. The climate remained dry and cool with further deforestation. However, the widely distributed yellowish silt clay suggests frequent floods in the region, resulting in a sharp reduction of settlement sites and the eventual decline of the Liangzhu Civilization