Effects of Temperature on the Proliferation of Human and Fish Lens Epithelial Cells

Crystallins are proteins that confer refractive properties to the crystalline lens. All vertebrate lenses contain α- and β-crystallins, and often a third major crystallin. Crystallins can have additional non-refractive functions; α-crystallins act as heat shock proteins, protecting lenses from heat-induced denaturation, and γ-crystallins are thought to be cryoproteins, protecting lenses from extreme cold. The concentration of α-crystallins is higher in mammalian lenses than in teleost lenses, while the opposite is true for γ-crystallins, suggesting that mammalian lenses would be better protected in warmer conditions and teleost lenses better protected in colder temperatures. This study determined whether temperature affects the growth of lens epithelial cells (LECs) derived from human and fish lenses. Both human and rainbow trout fish LECs were cultured (n = 4 each) and grown for 1, 2, 4, 6, 8 and 12 days at the optimal (37°C and 18°C, respectively), higher than optimal (42°C and 25°C, respectively) and lower than optimal temperatures (32°C and 10°C, respectively). At optimal temperatures, both fish and human LECs grew optimally. Higher temperatures were more deleterious to the proliferation index than lower temperatures for both human and fish LECs. Mitotic cells were non-existent in fish LECs grown at high temperatures. The sizes of the cells did not greatly change with temperature with either species, but human cells at non-optimal temperature tended to clump over time. Human LECs at the optimal temperature maintained their random distribution. Fish LECs at optimal temperatures moved from a random distribution to a clumped distribution, but lower temperatures had the opposite effect; LECs moved from a clumped to a random distribution. Only the high temperature group of fish LECs maintain their random organisation

Human Health Indicator Prediction from Gait Video

Body Mass Index (BMI), age, height and weight are important indicators of human health conditions, which can provide useful information for plenty of practical purposes, such as health care, monitoring and re-identification. Most existing methods of health indicator prediction mainly use front-view body or face images. These inputs are hard to be obtained in daily life and often lead to the lack of robustness for the models, considering their strict requirements on view and pose. In this paper, we propose to employ gait videos to predict health indicators, which are more prevalent in surveillance and home monitoring scenarios. However, the study of health indicator prediction from gait videos using deep learning was hindered due to the small amount of open-sourced data. To address this issue, we analyse the similarity and relationship between pose estimation and health indicator prediction tasks, and then propose a paradigm enabling deep learning for small health indicator datasets by pre-training on the pose estimation task. Furthermore, to better suit the health indicator prediction task, we bring forward Global-Local Aware aNd Centrosymmetric Encoder (GLANCE) module. It first extracts local and global features by progressive convolutions and then fuses multi-level features by a centrosymmetric double-path hourglass structure in two different ways. Experiments demonstrate that the proposed paradigm achieves state-of-the-art results for predicting health indicators on MoVi, and that the GLANCE module is also beneficial for pose estimation on 3DPW

Preliminary Study on Functional and Aesthetic Reconstruction by Using a Small Artery-only Free Medial Flap of the Second Toe for Fingertip Injuries

OBJECTIVES: This study was designed to introduce the feasibility of fingertip reconstruction by using a free medial flap of the second toe without vein anastomosis. METHODS: In total, 8 patients with fingertip injuries were treated successfully with this method. Patients who underwent reconstruction from September 2016 to October 2017 in our hospital with an artery-only free medial flap transfer of the second toe for fingertip injuries were included, and patients who underwent additional procedures that may impact the postoperative results and were followed up for less than 6 months were excluded. Clinical trial registration: ChiCTR19000021883. RESULTS: According to the Allen classification, five patients had Type 3 injuries, and three patients had Type 4 injuries. One arterial nerve and one digital nerve were repaired at the same time. No additional dissection was performed in either the donor or recipient site of the dorsal or volar vein. Postoperative venous congestion was monitored based on the color, temperature and the degree of tissue oxygen saturation. The flap size ranged from 1.20*1.0 cm2 to 1.80*1.0 cm2 . The reconstruction time was 71.86 (SD 14.75) minutes. The two-point discrimination and the monofilament results were satisfying; cold intolerance did not appear in five patients, and the other three patients had cold intolerance with grades of 4, 12 and 26, which were considered satisfactory. Moreover, leech therapy, continuous bleeding and needle sutures were not utilized in any cases. CONCLUSIONS: Reconstruction with a small artery-only free medial flap transfer of the second toe led to satisfactory sensory and motor function in the selected patients with fingertip injuries

Data Poisoning Attacks Against Multimodal Encoders

Traditional machine learning (ML) models usually rely on large-scale labeled datasets to achieve strong performance. However, such labeled datasets are often challenging and expensive to obtain. Also, the predefined categories limit the model's ability to generalize to other visual concepts as additional labeled data is required. On the contrary, the newly emerged multimodal model, which contains both visual and linguistic modalities, learns the concept of images from the raw text. It is a promising way to solve the above problems as it can use easy-to-collect image-text pairs to construct the training dataset and the raw texts contain almost unlimited categories according to their semantics. However, learning from a large-scale unlabeled dataset also exposes the model to the risk of potential poisoning attacks, whereby the adversary aims to perturb the model's training dataset to trigger malicious behaviors in it. Previous work mainly focuses on the visual modality. In this paper, we instead focus on answering two questions: (1) Is the linguistic modality also vulnerable to poisoning attacks? and (2) Which modality is most vulnerable? To answer the two questions, we conduct three types of poisoning attacks against CLIP, the most representative multimodal contrastive learning framework. Extensive evaluations on different datasets and model architectures show that all three attacks can perform well on the linguistic modality with only a relatively low poisoning rate and limited epochs. Also, we observe that the poisoning effect differs between different modalities, i.e., with lower MinRank in the visual modality and with higher Hit@K when K is small in the linguistic modality. To mitigate the attacks, we propose both pre-training and post-training defenses. We empirically show that both defenses can significantly reduce the attack performance while preserving the model's utility