Auricle shaping using 3D printing and autologous diced cartilage.

ObjectiveTo reconstruct the auricle using a porous, hollow, three-dimensional (3D)-printed mold and autologous diced cartilage mixed with platelet-rich plasma (PRP).MethodsMaterialise Magics v20.03 was used to design a 3D, porous, hollow auricle mold. Ten molds were printed by selective laser sintering with polyamide. Cartilage grafts were harvested from one ear of a New Zealand rabbit, and PRP was prepared using 10 mL of auricular blood from the same animal. Ear cartilage was diced into 0.5- to 2.0-mm pieces, weighed, mixed with PRP, and then placed inside the hollow mold. Composite grafts were then implanted into the backs of respective rabbits (n = 10) for 4 months. The shape and composition of the diced cartilage were assessed histologically, and biomechanical testing was used to determine stiffness.ResultsThe 3D-printed auricle molds were 0.6-mm thick and showed connectivity between the internal and external surfaces, with round pores of 0.1 to 0.3 cm. After 4 months, the diced cartilage pieces had fused into an auricular shape with high fidelity to the anthropotomy. The weight of the diced cartilage was 5.157 ± 0.230 g (P > 0.05, compared with preoperative). Histological staining showed high chondrocyte viability and the production of collagen II, glycosaminoglycans, and other cartilaginous matrix components. In unrestricted compression tests, auricle stiffness was 0.158 ± 0.187 N/mm, similar to that in humans.ConclusionAuricle grafts were constructed successfully through packing a 3D-printed, porous, hollow auricle mold with diced cartilage mixed with PRP. The auricle cartilage contained viable chondrocytes, appropriate extracellular matrix components, and good mechanical properties.Levels of evidenceNA. Laryngoscope, 129:2467-2474, 2019

Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision

In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS), which aims to segment objects of arbitrary classes instead of pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, and aligns the group tokens to the corresponding caption embedding. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given the group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, which encourages the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on three benchmark datasets, PASCAL VOC 2012, PASCAL Context, and COCO Object. Our model achieves superior segmentation results over the state-of-the-art method by using only 3\% data (4M vs 134M) for pre-training. Code and pre-trained models will be released for future research

Harnessing high-dimensional hyperentanglement through a biphoton frequency comb

Quantum entanglement is a fundamental resource for secure information processing and communications, where hyperentanglement or high-dimensional entanglement has been separately proposed towards high data capacity and error resilience. The continuous-variable nature of the energy-time entanglement makes it an ideal candidate for efficient high-dimensional coding with minimal limitations. Here we demonstrate the first simultaneous high-dimensional hyperentanglement using a biphoton frequency comb to harness the full potential in both energy and time domain. The long-postulated Hong-Ou-Mandel quantum revival is exhibited, with up to 19 time-bins, 96.5% visibilities. We further witness the high-dimensional energy-time entanglement through Franson revivals, which is observed periodically at integer time-bins, with 97.8% visibility. This qudit state is observed to simultaneously violate the generalized Bell inequality by up to 10.95 deviations while observing recurrent Clauser-Horne-Shimony-Holt S-parameters up to 2.76. Our biphoton frequency comb provides a platform in photon-efficient quantum communications towards the ultimate channel capacity through energy-time-polarization high-dimensional encoding

Near-infrared Hong-Ou-Mandel interference on a silicon quantum photonic circuit

Near-infrared Hong-Ou-Mandel quantum interference is observed in silicon nanophotonic directional couplers with raw visibilities on-chip at 90.5%. Spectrally-bright 1557-nm two-photon states are generated in a periodically-poled KTiOPO4 waveguide chip, serving as the entangled photon source and pumped with a self-injection locked laser, for the photon statistical measurements. Efficient four-port coupling in the communications C-band and in the high-index-contrast silicon photonics platform is demonstrated, with matching theoretical predictions of the quantum interference visibility. Constituents for the residual quantum visibility imperfection are examined, supported with theoretical analysis of the sequentially-triggered multipair biphoton contribution and techniques for visibility compensation, towards scalable high-bitrate quantum information processing and communications.Comment: 15 pages, 6 figure

KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image

Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a TV(total variation) loss function was applied to eliminate noise. Our method was trained on the GladNet dataset, known for its diverse collection of low-light images, tested against the Low-Light dataset, and evaluated using the ExDark dataset for downstream tasks, demonstrating competitive performance with a PSNR of 19.7216 and SSIM of 0.8213.Comment: Accepted by Signal, Image and Video Processin

How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs

This work focuses on the potential of Vision LLMs (VLLMs) in visual reasoning. Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness. For the OOD evaluation, we present two novel VQA datasets, each with one variant, designed to test model performance under challenging conditions. In exploring adversarial robustness, we propose a straightforward attack strategy for misleading VLLMs to produce visual-unrelated responses. Moreover, we assess the efficacy of two jailbreaking strategies, targeting either the vision or language component of VLLMs. Our evaluation of 21 diverse models, ranging from open-source VLLMs to GPT-4V, yields interesting observations: 1) Current VLLMs struggle with OOD texts but not images, unless the visual information is limited; and 2) These VLLMs can be easily misled by deceiving vision encoders only, and their vision-language training often compromise safety protocols. We release this safety evaluation suite at https://github.com/UCSC-VLAA/vllm-safety-benchmark.Comment: H.T., C.C., and Z.W. contribute equally. Work done during H.T. and Z.W.'s internship at UCSC, and C.C. and Y.Z.'s internship at UN

Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos

Understanding human emotions is a crucial ability for intelligent robots to provide better human-robot interactions. The existing works are limited to trimmed video-level emotion classification, failing to locate the temporal window corresponding to the emotion. In this paper, we introduce a new task, named Temporal Emotion Localization in videos~(TEL), which aims to detect human emotions and localize their corresponding temporal boundaries in untrimmed videos with aligned subtitles. TEL presents three unique challenges compared to temporal action localization: 1) The emotions have extremely varied temporal dynamics; 2) The emotion cues are embedded in both appearances and complex plots; 3) The fine-grained temporal annotations are complicated and labor-intensive. To address the first two challenges, we propose a novel dilated context integrated network with a coarse-fine two-stream architecture. The coarse stream captures varied temporal dynamics by modeling multi-granularity temporal contexts. The fine stream achieves complex plots understanding by reasoning the dependency between the multi-granularity temporal contexts from the coarse stream and adaptively integrates them into fine-grained video segment features. To address the third challenge, we introduce a cross-modal consensus learning paradigm, which leverages the inherent semantic consensus between the aligned video and subtitle to achieve weakly-supervised learning. We contribute a new testing set with 3,000 manually-annotated temporal boundaries so that future research on the TEL problem can be quantitatively evaluated. Extensive experiments show the effectiveness of our approach on temporal emotion localization. The repository of this work is at https://github.com/YYJMJC/Temporal-Emotion-Localization-in-Videos.Comment: Accepted by ACM Multimedia 202

Beneficial effects and safety of traditional Chinese medicine for chronic inflammatory demyelinating polyradiculoneuropathy: A case report and literature review

Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is an immune-mediated neuropathy. First-line treatments for CIDP include corticosteroids, intravenous immunoglobulin, and plasma exchange. However, the application is always limited by high costs, effectiveness, and adverse events. This study investigated a new potentially effective and safe therapeutic treatment to alleviate CIDP symptoms and improve the quality of life. In the present case, a 47-year-old rural woman presented with weakness and numbness of progressive extremities. She was diagnosed with CIDP based on abnormal cerebrospinal fluid and electromyography. The patient was treated with intravenous dexamethasone for 1 week and with Huangqi-Guizhi-Wuwu and Bu-Yang-Huan-Wu decoctions for 90 days. Surprisingly, after the treatment, the weakness and numbness were eliminated, and the quality of life improved. The varying INCAT, MRC, and BI scores also reflected the treatment effects. After 8 months of discharge, the symptoms did not relapse during the follow-up. We also searched “traditional Chinese medicine (TCM)” and “CIDP” in PubMed, EMBASE, the Web of Science, the Cochrane Library, the Chinese National Knowledge Infrastructure Databases, Wanfang Data, and the Chongqing Chinese Science and Technology Periodical Database. Finally, only ten studies were included in the literature review. Three studies were randomized controlled trials, and seven were case reports or case series. There were 419 CIDP patients, but all study sites were in China. Nine TCM formulas involving 44 herbs were reported, with Huang Qi (Astragalus membranaceus) being the most important herb. In conclusion, the case and literature demonstrated that TCM treatment might be a more effective, low-cost, and safe option for treating CIDP. Although these preliminary findings are promising, a larger sample size and higher-quality randomized clinical trials are urgently required to confirm our findings

Body composition parameters correlate with the endoscopic severity in Crohn’s disease patients treated with infliximab

BackgroundThe disease activity status and behavior of Crohn’s disease (CD) can reflect the severity of the disease, and changes in body composition are common in CD patients.AimsThe aim of this study was to investigate the relationship between body composition parameters and disease severity in CD patients treated with infliximab (IFX).MethodsPatients with CD assessed with the simple endoscopic score (SES-CD) and were treated with IFX were retrospectively collected, and body composition parameters at the level of the 3rd lumbar vertebrae were calculated from computed tomography (CT) scans of the patients. The correlation of patients’ body composition parameters with disease activity status and disease behavior was analyzed, and the diagnostic value of the relevant parameters was assessed using receiver operating characteristic (ROC) curves.ResultsA total of 106 patients were included in this study. There were significant differences in the subcutaneous adiposity index (SAI) (p = 0.010), the visceral adiposity index (VAI) (p < 0.001), the skeletal muscle mass index (SMI) (p < 0.001), and decreased skeletal muscle mass (p < 0.001) among patients with different activity status. After Spearman and multivariate regression analysis, SAI (p = 0.006 and p = 0.001), VAI (p < 0.001 and p < 0.001), and SMI (p < 0.001and p = 0.007) were identified as independent correlates of disease activity status (both disease activity and moderate-to-severe activity), with disease activity status independently positively correlated with SAI and SMI and independently negatively correlated with VAI. In determining the disease activity and moderate-to-severe activity status, SMI performed best relative to SAI and VAI, with areas under the ROC curve of 0.865 and 0.801, respectively. SAI (p = 0.015), SMI (p = 0.011) and decreased skeletal muscle mass (p = 0.027) were significantly different between different disease behavior groups (inflammatory disease behavior group, complex disease behavior group) but were not independent correlates (p > 0.05).ConclusionBody composition parameters of CD patients treated with IFX correlate with the endoscopic disease severity, and SMI can be used as a reliable indicator of disease activity status

Genomic traits of multidrug resistant enterotoxigenic Escherichia coli isolates from diarrheic pigs

Diarrhea caused by enterotoxigenic Escherichia coli (ETEC) infections poses a significant challenge in global pig farming. To address this issue, the study was conducted to identify and characterize 19 ETEC isolates from fecal samples of diarrheic pigs sourced from large-scale farms in Sichuan Province, China. Whole-genome sequencing and bioinformatic analysis were utilized for identification and characterization. The isolates exhibited substantial resistance to cefotaxime, ceftriaxone, chloramphenicol, ciprofloxacin, gentamicin, ampicillin, tetracycline, florfenicol, and sulfadiazine, but were highly susceptible to amikacin, imipenem, and cefoxitin. Genetic diversity among the isolates was observed, with serotypes O22:H10, O163orOX21:H4, and O105:H8 being dominant. Further analysis revealed 53 resistance genes and 13 categories of 195 virulence factors. Of concern was the presence of tet(X4) in some isolates, indicating potential public health risks. The ETEC isolates demonstrated the ability to produce either heat-stable enterotoxin (ST) alone or both heat-labile enterotoxin (LT) and ST simultaneously, involving various virulence genes. Notably, STa were linked to human disease. Additionally, the presence of 4 hybrid ETEC/STEC isolates harboring Shiga-like toxin-related virulence factors, namely stx2a, stx2b, and stx2e-ONT-2771, was identified. IncF plasmids carrying multiple antimicrobial resistance genes were prevalent, and a hybrid ETEC/STEC plasmid was detected, highlighting the role of plasmids in hybrid pathotype emergence. These findings emphasized the multidrug resistance and pathogenicity of porcine-origin ETEC strains and the potential risk of epidemics through horizontal transmission of drug resistance, which is crucial for effective control strategies and interventions to mitigate the impact on animal and human health