Protection of magnesium alloys from corrosion using magnesium rich surfaces.

Mg alloys have great potential in engineering applications for saving energy consumption due to the high strength to weight ratio. Mg alloys are also biocompatible and biodegradable with biomedical applications such as orthopaedic and vascular implants. Controlling the corrosion of Mg alloy components is necessary to sustain their performance over the design lifespan. A low corrosion rate is also preferred for implants to mitigate negative effects such as hydrogen evolution during corrosion. Surface films can be used to control the corrosion of an Mg alloy effectively. In this work, Mg(OH)₂ coatings were deposited on Mg alloy substrates (AZ31 and ZM21) by hydrothermal (H.T.) steam treatment as a benchmark and subsequently by novel processing using electrochemical (E.C.) and additive treatment with an Mg²⁺ rich solution. The microstructures and compositions of the alloys are characterised both with and without coatings. Corrosion tests were conducted in various test solutions, including 3.5% NaCl, 0.9% NaCl and Hank's solutions. Electrochemical techniques and mass change measurement are used for the corrosion testing of initial exposure and longer-term immersion, respectively. The processing parameters of the electrochemical and additive methods were optimised based on the corrosion behaviour of the coated samples. This research shows that the Mg(OH)₂ based coating can enhance the corrosion protection of the Mg alloy substrates, with at least a 3 fold reduction in corrosion rates compared to uncoated substrates. Comparing hydrothermal coatings, the electrochemical and additive (EC+Additive) coatings not only show similar corrosion performance but also greater manufacturing flexibility and repairability with potential for further enhancement.PhD in Transport System

Enzyme farming – bringing green and safety to farmland

Garbage enzymes rationally using of waste from fruits and vegetables are rich in nutritionally active substances and microbial groups and positively affect the growth of agricultural crops. In this project, by studying the effect of different classes of garbage enzymes on plant growth at different concentrations, and study on molecular biological mechanism will provide sufficient theoretical support for the development of environmental friendly irrigation machine in agriculture

Transcriptome and Network Changes in Climbers at Extreme Altitudes

Extreme altitude can induce a range of cellular and systemic responses. Although it is known that hypoxia underlies the major changes and that the physiological responses include hemodynamic changes and erythropoiesis, the molecular mechanisms and signaling pathways mediating such changes are largely unknown. To obtain a more complete picture of the transcriptional regulatory landscape and networks involved in extreme altitude response, we followed four climbers on an expedition up Mount Xixiabangma (8,012 m), and collected blood samples at four stages during the climb for mRNA and miRNA expression assays. By analyzing dynamic changes of gene networks in response to extreme altitudes, we uncovered a highly modular network with 7 modules of various functions that changed in response to extreme altitudes. The erythrocyte differentiation module is the most prominently up-regulated, reflecting increased erythrocyte differentiation from hematopoietic stem cells, probably at the expense of differentiation into other cell lineages. These changes are accompanied by coordinated down-regulation of general translation. Network topology and flow analyses also uncovered regulators known to modulate hypoxia responses and erythrocyte development, as well as unknown regulators, such as the OCT4 gene, an important regulator in stem cells and assumed to only function in stem cells. We predicted computationally and validated experimentally that increased OCT4 expression at extreme altitude can directly elevate the expression of hemoglobin genes. Our approach established a new framework for analyzing the transcriptional regulatory network from a very limited number of samples

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Acute clouding of trifocal lens during implantation: a case report

Abstract Background Intraoperative IOLs clouding of several kinds of hydrophilic acrylic intraocular lenses (IOLs) have been reported due to temperature changes. This phenomenon reported previously occurred in cold countries and during the winter months. However, no clinical case was reported about trifocal IOL opacification during operation. We report a case in which acute opacification of the optical region occurred simultaneously when AT LISA tri 839mp(Carl Zeiss) was implanted into the eye. Case presentation A 79-year-old woman with a cortex and nucleus cataract was scheduled to undergo right eye phacoemulsification assisted by femtosecond technique. The trifocal lens (AT LISA tri 839mp), which is made of hydrophilic acrylic (25%) with hydrophobic surface properties, was chosen for implantation. As the IOL was implanted into the eye, it became cloudy immediately. Then it was replaced by another AT LISA tri 839mp, which was transferred from lens company outside, the same phenomenon was observed. These two lenses underwent the same temperature fluctuation from cold outside to operating room. Finally, a ZCB00 (Allergan) was implanted. Conclusions The acute intraoperative clouding of trifocal lens(AT LISA tri 839mp) results from fluctuation of temperature should be noticed

ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest

Abstract Background Cancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is difficult to obtain satisfactory classification results. Meanwhile, some cancers have been well studied and classified to some subtypes, which are adopt by most researchers. Hence, this priori knowledge is significant for further identifying new meaningful subtypes. Results In this paper, we present a combined parallel random forest and autoencoder approach for cancer subtype identification based on high dimensional gene expression data, ForestSubtype. ForestSubtype first adopts the parallel RF and the priori knowledge of cancer subtype to train a module and extract significant candidate features. Second, ForestSubtype uses a random forest as the base module and ten parallel random forests to compute each feature weight and rank them separately. Then, the intersection of the features with the larger weights output by the ten parallel random forests is taken as our subsequent candidate features. Third, ForestSubtype uses an autoencoder to condenses the selected features into a two-dimensional data. Fourth, ForestSubtype utilizes k-means++ to obtain new cancer subtype identification results. In this paper, the breast cancer gene expression data obtained from The Cancer Genome Atlas are used for training and validation, and an independent breast cancer dataset from the Molecular Taxonomy of Breast Cancer International Consortium is used for testing. Additionally, we use two other cancer datasets for validating the generalizability of ForestSubtype. ForestSubtype outperforms the other two methods in terms of the distribution of clusters, internal and external metric results. The open-source code is available at https://github.com/lffyd/ForestSubtype . Conclusions Our work shows that the combination of high-dimensional gene expression data and parallel random forests and autoencoder, guided by a priori knowledge, can identify new subtypes more effectively than existing methods of cancer subtype classification