Skin Mast Cells Contribute to Sporothrix schenckii Infection

Background: Sporothrix schenckii (S. schenckii), a dimorphic fungus, causes sporotrichosis. Mast cells (MCs) have been described to be involved in skin fungal infections. The role of MCs in cutaneous sporotrichosis remains largely unknown. Objectives: To characterize the role and relevance of MCs in cutaneous sporotrichosis. Methods: We analyzed cutaneous sporotrichosis in wild-type (WT) mice and two different MC-deficient strains. In vitro, MCs were assessed for S. schenckii-induced cytokine production and degranulation after incubation with S. schenckii. We also explored the role of MCs in human cutaneous sporotrichosis. Results: WT mice developed markedly larger skin lesions than MC-deficient mice (> 1.5 fold) after infection with S. schenckii, with significantly increased fungal burden. S. schenckii induced the release of tumor necrosis factor alpha (TNF), interleukin (IL)-6, IL-10, and IL-1β by MCs, but not degranulation. S. schenckii induced larger skin lesions and higher release of IL-6 and TNF by MCs as compared to the less virulent S. albicans. In patients with sporotrichosis, TNF and IL-6 were increased in skin lesions, and markedly elevated levels in the serum were linked to disease activity. Conclusions: These findings suggest that cutaneous MCs contribute to skin sporotrichosis by releasing cytokines such as TNF and IL-6

A Dynamic Points Removal Benchmark in Point Cloud Maps

In the field of robotics, the point cloud has become an essential map representation. From the perspective of downstream tasks like localization and global path planning, points corresponding to dynamic objects will adversely affect their performance. Existing methods for removing dynamic points in point clouds often lack clarity in comparative evaluations and comprehensive analysis. Therefore, we propose an easy-to-extend unified benchmarking framework for evaluating techniques for removing dynamic points in maps. It includes refactored state-of-art methods and novel metrics to analyze the limitations of these approaches. This enables researchers to dive deep into the underlying reasons behind these limitations. The benchmark makes use of several datasets with different sensor types. All the code and datasets related to our study are publicly available for further development and utilization.Comment: Code check https://github.com/KTH-RPL/DynamicMap_Benchmark.git , 7 pages, accepted by ITSC 202

A Dynamic Points Removal Benchmark in Point Cloud Maps

<p>Uniformat Dataset LiDAR Point Cloud Data [PCD format]<br> check <a href="https://github.com/KTH-RPL/DynamicMap_Benchmark">DynamicMap_Benchmark repo</a> and Our Papers for more detail.</p> <ul> <li>00: KITTI sequence 00 [VLP-64] from frame 4390 to 4530</li> <li>05: KITTI sequence 05 [VLP-64] from frame 2350 to 2670</li> <li>av2: Argoverse 2.0 one sequence on <em>07YOTznatmYypvQYpzviEcU3yGPsyaGg__Spring_2020. </em>[2 x VLP-32]</li> <li>semindoor: semi-indoor dataset collected by [VLP-16], collected by ourselves.</li> </ul> <p> </p> <table> <tbody> <tr> <td>Dataset</td> <td>Description</td> <td>Sensor Type</td> <td>Total Frame Number</td> </tr> <tr> <td>KITTI sequence 00</td> <td>in a small town with few dynamics (including one pedestrian around</td> <td>VLP-64</td> <td>141</td> </tr> <tr> <td>KITTI sequence 05</td> <td>in a small town straight way, one higher car, the benchmarking paper cover image from this sequeue</td> <td>VLP-64</td> <td>321</td> </tr> <tr> <td>Argoverse2</td> <td>in a big city, crowded and tall buildings (including cyclists, vehicles, people walking near the building etc.</td> <td>2 x VLP-32</td> <td>575</td> </tr> <tr> <td>Semi-indoor</td> <td>Collected by us, running on small 1x2 vehicle with two people walking around the platform</td> <td>VLP-16</td> <td>960</td> </tr> </tbody> </table> <p>Cite as:</p> <pre><code class="language-bash">@article{zhang2023benchmark, author={Zhang, Qingwen and Duberg, Daniel and Geng, Ruoyu and Jia, Mingkai and Wang, Lujia and Jensfelt, Patric}, title={A Dynamic Points Removal Benchmark in Point Cloud Maps}, journal={arXiv preprint arXiv:2307.07260}, year={2023} }</code></pre> <p> </p&gt

Fluorogen–Peptide Conjugates with Tunable Aggregation-Induced Emission Characteristics for Bioprobe Design

Fluorogens with aggregation-induced emission (AIE) characteristics are attracting intense research interest, and an AIE–peptide conjugate strategy has been reported for developing turn-on probes based on hydrophilic peptide ligands. To build a model also suitable for hydrophobic ligands, we propose to fine-tune the AIE characteristics for probe design. In this work, an iconic AIE fluorogen tetraphenylethene (TPE) was designed to conjugate with peptide fragments containing different numbers of aspartic acid (D) units. Relationships between the numbers of D and the hydrophilicity, optical properties, and aggregate sizes and the AIE characteristics of TPE–peptide conjugates were investigated carefully. Five carboxyl groups were found to be the threshold to “turn off” the fluorescence of TPE. As a proof-of-concept, TPE-SS-D₅ containing a cleavable disulfide bond was synthesized for thiol turn-on detection. The validated tunable AIE characteristic offers new opportunities to design fluorescence turn-on probes based on hydrophobic recognition elements and AIE fluorogens