Interactions between Inflammatory Cells and CoCrMo Alloy Surfaces under Simulated Inflammatory Conditions

Metallic biomaterials continue to be the primary materials for medical devices because of their excellent physical and mechanical properties especially for certain implants which need high strength (hip, spinal, shoulder, knee etc.). However there is no perfect material to meet all the requirements of a medical device. Metallic biomaterials are prone to corrosion and wear which has been associated with implant failure (osteolysis, aseptic loosening, pseudo tumor etc.) This study focused on investigating how inflammatory cells interact with CoCrMo alloy surfaces under simulated inflammatory conditions (H2O2 treatment). It is hypothesized that the presence of Reactive Oxygen Species (ROS) in cell culture will affect the viability of inflammatory cells and that when ROS interacts with CoCrMo alloy surfaces the effect on cell viability is increased. The first part of the study focused on the cell behavior (viability and morphology) when cultured directly on CoCrMo alloy surfaces with different concentrations of H2O2 in the medium (0.2 mM – 1 mM) and compared with the same solution conditions but on tissue culture substrate. The electrochemical behavior of CoCrMo alloy surfaces was also examined when exposed to inflammatory cells and inflammatory fluid (i.e. medium with H2O2 added). The results show a narrower range of H2O2 concentration for cell viability for the CoCrMo group when compared to tissue culture substrate (p \u3c 0.05). This indicates that the presence of metal implants during inflammation might raise the ROS toxicity towards cells after surgery. Inflammatory conditions (i.e. H2O2 + inflammatory cells) were also found to result in corrosion of CoCrMo alloy surfaces. These short-term results raise significant questions about the long-term interactions between ROS, CoCrMo alloy and inflammatory cells

Preparation of Green Biosorbent using Rice Hull to Preconcentrate, Remove and Recover Heavy Metal and Other Metal Elements from Water

Sodium hydroxide treated rice hulls were investigated to preconcentrate, remove, and recover metal ions including Be2+, Al3+, Cr3+, CO2+, Ni2+, Cu2+, Zn2+, Sr2+, Ag+, Cd2+, Ba2+, and Pb2+ in both batch mode and column mode. Sodium hydroxide treatment significantly improved the removal efficiency for all metal ions of interest compared to the untreated rice hull. The removal kinetics were extremely fast for Co, Ni, Cu, Zn, Sr, Cd, and Ba, which made the treated rice hull a promising economic green adsorbent to preconcentrate, remove, and recover low-level metal ions in column mode at relatively high throughput. The principal removal mechanism is believed to be the electrostatic attraction between the negatively charged rice hulls and the positively charged metal ions. pH had a drastic impact on the removal for different metal ions and a pH of 5 worked best for most of the metal ions of interest. Processed rice hulls provide an economic alternative to costly resins that are currently commercially available products designed for metal ion preconcentration for trace metal analysis, and more importantly, for toxic heavy metal removal and recovery from the environment

E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking

In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on blind flexible selfdocking, where we aim to predict the positions, orientations and conformations of docked molecules. Traditional physics-based methods usually suffer from inaccurate scoring functions and high inference cost. Recently, data-driven methods based on deep learning techniques are attracting growing interest thanks to their efficiency during inference and promising performance. These methods usually either adopt a two-stage approach by first predicting the distances between proteins and ligands and then generating the final coordinates based on the predicted distances, or directly predicting the global roto-translation of ligands. In this paper, we take a different route. Inspired by the resounding success of AlphaFold2 for protein structure prediction, we propose E3Bind, an end-to-end equivariant network that iteratively updates the ligand pose. E3Bind models the protein-ligand interaction through careful consideration of the geometric constraints in docking and the local context of the binding site. Experiments on standard benchmark datasets demonstrate the superior performance of our end-to-end trainable model compared to traditional and recently-proposed deep learning methods.Comment: International Conference on Learning Representations (ICLR 2023

Deep Learning Methods for Calibrated Photometric Stereo and Beyond

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models.Comment: 19 pages, 11 figures, 4 table

Impact of anthocyanins derived from Dioscorea alata L. on growth performance, carcass characteristics, antioxidant capacity, and immune function of Hainan black goats

Dioscorea alata L. anthocyanins (DAC) are natural compounds found in plants and have shown potential health benefits. The objective of this investigation was to assess the impact of anthocyanins sourced from Dioscorea alata L. on the growth, carcass traits, antioxidant potential, and immune response of Hainan black goats. In this study, 30 three-month-old Hainan black goats (with a weight of 11.30 ± 0.82 kg) were selected and randomly divided into two groups, with 15 goats in each group. During the 60-day experiment, the control group (CON) and the treatment group (DAC) were, respectively, supplemented with 0 and 40 mg/kg BW of DAC in the basal diet. The results showed that DAC had no significant impact on the growth performance and body characteristics of Hainan black goats (p > 0.05). However, in terms of meat quality, the addition of DAC significantly increased the pH value and cooking yield 24 h post-slaughter (p < 0.05), while reducing the shear force of the meat (p < 0.05). Compared to the control group, adding DAC to the feed resulted in a significant increase in the total antioxidant capacity (T-AOC) and superoxide dismutase (T-SOD) concentrations in plasma after 30 days of feeding (p < 0.05). After 60 days of feeding, the concentrations of T-AOC, T-SOD, glutathione peroxidase (GSH-Px), and catalase (CAT) in the plasma of the DAC group was higher than that of the control group (p < 0.05), while the concentration of malondialdehyde (MDA) was lower than that of the control group (p < 0.05). In addition, supplementing DAC significantly increased the content of interleukin-10 (IL-10) and immunoglobulin M (IgM) in the plasma of Hainan black goats after 30 days of feeding (p < 0.05), while reducing the content of interleukin-6 (IL-6) (p < 0.05). After 60 days of feeding, the immunoglobulin G (IgG) and IL-10 content in the plasma of the DAC group was significantly increased (p < 0.05), while the concentrations of IL-1β, IL-6, and tumor necrosis factor-α (TNF-α) were suppressed (p < 0.05). In summary, these results indicate that supplementing DAC can improve the meat quality, enhance the antioxidant capacity, and immune function of Hainan black goats

A Semi-Supervised Synthetic Aperture Radar (SAR) Image Recognition Algorithm Based on an Attention Mechanism and Bias-Variance Decomposition

Synthetic Aperture Radar (SAR) target recognition is an important research direction of SAR image interpretation. In recent years, most of machine learning methods applied to SAR target recognition are supervised learning which requires a large number of labeled SAR images. However, labeling SAR images is expensive and time-consuming. We hereby propose an end-to-end semi-supervised recognition method based on an attention mechanism and bias-variance decomposition, which focuses on the unlabeled data screening and pseudo-labels assignment. Different from other learning methods, the training set in each iteration is determined by a module that we here propose, called dataset attention module (DAM). Through DAM, the contributing unlabeled data will have more possibilities to be added into the training set, while the non-contributing and hard-to-learn unlabeled data will receive less attention. During the training process, each unlabeled data will be input into the network for prediction. The pseudo-label of the unlabeled data is considered to be the most probable classification in the multiple predictions, which reduces the risk of the single prediction. We calculate the prediction bias-and-variance of all the unlabeled data and use the result as the criteria to screen the unlabeled data in DAM. In this paper, we carry out semi-supervised learning experiments under different unlabeled rates on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The recognition accuracy of our method is better than several state of the art semi-supervised learning algorithms