On translating technology research into industrial applications

Using personal cases he previously experienced, Prof Wong will share with colleagues the influence of environments and market forces that drive the applied technology research into practice. The speech will be delivered in an informal, interactive format, rather than didactic

LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images

Diffusion-weighted (DW) magnetic resonance imaging is essential for the diagnosis and treatment of ischemic stroke. DW images (DWIs) are usually acquired in multi-slice settings where lesion areas in two consecutive 2D slices are highly discontinuous due to large slice thickness and sometimes even slice gaps. Therefore, although DWIs contain rich 3D information, they cannot be treated as regular 3D or 2D images. Instead, DWIs are somewhere in-between (or 2.5D) due to the volumetric nature but inter-slice discontinuities. Thus, it is not ideal to apply most existing segmentation methods as they are designed for either 2D or 3D images. To tackle this problem, we propose a new neural network architecture tailored for segmenting highly-discontinuous 2.5D data such as DWIs. Our network, termed LambdaUNet, extends UNet by replacing convolutional layers with our proposed Lambda+ layers. In particular, Lambda+ layers transform both intra-slice and inter-slice context around a pixel into linear functions, called lambdas, which are then applied to the pixel to produce informative 2.5D features. LambdaUNet is simple yet effective in combining sparse inter-slice information from adjacent slices while also capturing dense contextual features within a single slice. Experiments on a unique clinical dataset demonstrate that LambdaUNet outperforms existing 3D/2D image segmentation methods including recent variants of UNet. Code for LambdaUNet is released with the publication to facilitate future research

Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation

We present a new encoder-decoder Vision Transformer architecture, Patcher, for medical image segmentation. Unlike standard Vision Transformers, it employs Patcher blocks that segment an image into large patches, each of which is further divided into small patches. Transformers are applied to the small patches within a large patch, which constrains the receptive field of each pixel. We intentionally make the large patches overlap to enhance intra-patch communication. The encoder employs a cascade of Patcher blocks with increasing receptive fields to extract features from local to global levels. This design allows Patcher to benefit from both the coarse-to-fine feature extraction common in CNNs and the superior spatial relationship modeling of Transformers. We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel. The use of MoE enables better specializations of the expert features and reduces interference between them during inference. Extensive experiments demonstrate that Patcher outperforms state-of-the-art Transformer- and CNN-based approaches significantly on stroke lesion segmentation and polyp segmentation. Code for Patcher is released with publication to facilitate future research.Comment: MICCAI 202

Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy

Characterization of a human tumorsphere glioma orthotopic model using magnetic resonance imaging

Magnetic resonance imaging (MRI) is the imaging modality of choice by which to monitor patient gliomas and treatment effects, and has been applied to murine models of glioma. However, a major obstacle to the development of effective glioma therapeutics has been that widely used animal models of glioma have not accurately recapitulated the morphological heterogeneity and invasive nature of this very lethal human cancer. This deficiency is being alleviated somewhat as more representative models are being developed, but there is still a clear need for relevant yet practical models that are well-characterized in terms of their MRI features. Hence we sought to chronicle the MRI profile of a recently developed, comparatively straightforward human tumor stem cell (hTSC) derived glioma model in mice using conventional MRI methods. This model reproduces the salient features of gliomas in humans, including florid neoangiogenesis and aggressive invasion of normal brain. Accordingly, the variable, invasive morphology of hTSC gliomas visualized on MRI duplicated that seen in patients, and it differed considerably from the widely used U87 glioma model that does not invade normal brain. After several weeks of tumor growth the hTSC model exhibited an MRI contrast enhancing phenotype having variable intensity and an irregular shape, which mimicked the heterogeneous appearance observed with human glioma patients. The MRI findings reported here support the use of the hTSC glioma xenograft model combined with MRI, as a test platform for assessing candidate therapeutics for glioma, and for developing novel MR methods

Development of a Bamlanivimab Infusion Process in the Emergency Department for Outpatient COVID-19 Patients

The coronavirus disease 2019 (COVID-19) pandemic has prompted the creation of new therapies to help fight against the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Bamlanivimab is a SARS-CoV-2 monoclonal antibody that is administered as an intravenous infusion to ambulatory patients with mild or moderate COVID-19, but a concern that arose was deciding the optimal location for patients to receive the medication. This report describes the development and implementation of a bamlanivimab infusion center in the emergency department of three hospitals in Orange County, California, shortly after bamlanivimab received emergency use authorization. As a result, a total of 601 patients received bamlanivimab in one of these three emergency departments between December 2020 to April 2021. The emergency department was shown to be an optimal setting for administration of bamlanivimab due to its convenience, accessibility, and capabilities for monitoring patients

3D Axon Structure Extraction and Analysis in Confocal Fluorescence Microscopy Images

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.Molecular and Cellular Biolog

Phenotypic analysis of images of zebrafish treated with Alzheimer's γ-secretase inhibitors

<p>Abstract</p> <p>Background</p> <p>Several γ-secretase inhibitors (GSI) are in clinical trials for the treatment of Alzheimer's disease (AD). This enzyme mediates the proteolytic cleavage of amyloid precursor protein (APP) to generate amyloid β protein, Aβ, the pathogenic protein in AD. The γ-secretase also cleaves Notch to generate Notch Intracellular domain (NICD), the signaling molecule that is implicated in tumorigenesis.</p> <p>Results</p> <p>We have developed a method to examine live zebrafish that were each treated with γ-secretase inhibitors (GSI), DAPT {N- [N-(3,5-Difluorophenacetyl-L-alanyl)]-S-phenylglycine <it>t</it>-Butyl Ester}, Gleevec, or fragments of Gleevec. These compounds were first tested in a cell-based assay and the effective concentrations of these compounds that blocked Aβ generation were quantitated. The mortality of zebrafish, as a result of exposure to different doses of compound, was assessed, and any apoptotic processes were examined by TUNEL staining. We then used conventional and automatic microscopes to acquire images of zebrafish and applied algorithms to automate image composition and processing. Zebrafish were treated in 96- or 384-well plates, and the phenotypes were analyzed at 2, 3 and 5 days post fertilization (dpf). We identified that AD95, a fragment of Gleevec, effectively blocks Aβ production and causes specific phenotypes that were different from those treated with DAPT. Finally, we validated the specificity of two Notch phenotypes (pigmentation and the curvature of tail/trunk) induced by DAPT in a dose-dependent manner. These phenotypes were examined in embryos treated with GSIs or AD95 at increasing concentrations. The expression levels of Notch target gene <it>her6 </it>were also measured by <it>in situ </it>hybridization and the co-relationship between the levels of Notch inhibition by DAPT and AD95 and the severity of phenotypes were determined.</p> <p>Conclusion</p> <p>The results reported here of the effects on zebrafish suggest that this newly developed method may be used to screen novel GSIs and other leads for a variety of therapeutic indications.</p

Custom-molded foot-orthosis intervention and multi-segment medial foot kinematics during walking

Context: Foot-orthosis (FO) intervention to prevent and treat numerous lower extremity injuries is widely accepted clinically. However, the results of quantitative gait analyses have been equivocal. The foot models used, participants receiving intervention, and orthoses used might contribute to the variability. Objective: To investigate the effect of a custom-molded FO intervention on multisegment medial foot kinematics during walking in participants with low-mobile foot posture. Design: Crossover study. Setting: University biomechanics and ergonomics laboratory. Patients or Other Participants: Sixteen participants with low-mobile foot posture (7 men, 9 women) were assigned randomly to 1 of 2 FO groups. Intervention(s): After a 2-week period to break in the FOs, individuals participated in a gait analysis that consisted of 5 successful walking trials (1.3 to 1.4 m/s) during no-FO and FO conditions. Main Outcome Measure(s): Three-dimensional displacements during 4 subphases of stance (loading response, mid- stance, terminal stance, preswing) were computed for each multisegment foot model articulation. Results: Repeated-measures analyses of variance (ANO- VAs) revealed that rearfoot complex dorsiflexion displacement during midstance was greater in the FO than the no-FO condition (F114 = 5.24, P=.O4, partial r|2 = 0.27). Terminal stance repeated-measures ANOVA results revealed insert-by-insert condition interactions for the first metatarsophalangeal ¡oint complex (F114=7.87, P=.O1, partial if = 0.36). However, additional follow-up analysis did not reveal differences between the no-FO and FO conditions for the balanced traditional ortho- sis (F, 14 = 4.32, P = .O8, partial if = 0.38) or full-contact orthosis (F1i14 = 4.10, P=.O8, partial if = 0.37). Conclusions: Greater rearfoot complex dorsiflexion during midstance associated with FO intervention may represent improved foot kinematics in people with low-mobile foot postures. Furthermore, FO intervention might partially correct dysfunctional kinematic patterns associated with low-mobile foot postures.