WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections

Background and objective: The COVID-19 pandemic is a major global health crisis of this century. The use of neural networks with CT imaging can potentially improve clinicians’ efficiency in diagnosis. Previous studies in this field have primarily focused on classifying the disease on CT images, while few studies targeted the localisation of disease regions. Developing neural networks for automating the latter task is impeded by limited CT images with pixel-level annotations available to the research community. Methods: This paper proposes a weakly-supervised framework named “Weak Variational Autoencoder for Localisation and Enhancement” (WVALE) to address this challenge for COVID-19 CT images. This framework includes two components: anomaly localisation with a novel WVAE model and enhancement of supervised segmentation models with WVALE. Results: The WVAE model have been shown to produce high-quality post-hoc attention maps with fine borders around infection regions, while weak supervision segmentation shows results comparable to conventional supervised segmentation models. The WVALE framework can enhance the performance of a range of supervised segmentation models, including state-of-art models for the segmentation of COVID-19 lung infection. Conclusions: Our study provides a proof-of-concept for weakly supervised segmentation and an alternative approach to alleviate the lack of annotation, while its independence from classification & segmentation frameworks makes it easily integrable with existing systems

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches

MEEDNets: Medical Image Classification via Ensemble Bio-inspired Evolutionary DenseNets

Inspired by the biological evolution, this paper proposes an evolutionary synthesis mechanism to automatically evolve DenseNet towards high sparsity and efficiency for medical image classification. Unlike traditional automatic design methods, this mechanism generates a sparser offspring in each generation based on its previous trained ancestor. Concretely, we use a synaptic model to mimic biological evolution in the asexual reproduction. Each generation's knowledge is passed down to its descendant, and an environmental constraint limits the size of the descendant evolutionary DenseNet, moving the evolution process towards high sparsity. Additionally, to address the limitation of ensemble learning that requires multiple base networks to make decisions, we propose an evolution-based ensemble learning mechanism. It utilises the evolutionary synthesis scheme to generate highly sparse descendant networks, which can be used as base networks to perform ensemble learning in inference. This is specially useful in the extreme case when there is only a single network. Finally, we propose the MEEDNets (Medical Image Classification via Ensemble Bio-inspired Evolutionary DenseNets) model which consists of multiple evolutionary DenseNet-121s synthesised in the evolution process. Experimental results show that our bio-inspired evolutionary DenseNets are able to drop less important structures and compensate for the increasingly sparse architecture. In addition, our proposed MEEDNets model outperforms the state-of-the-art methods on two publicly accessible medical image datasets. All source code of this study is available at https://github.com/hengdezhu/MEEDNets.</p

Downregulation of PKCζ/Pard3/Pard6b is responsible for lung adenocarcinoma cell EMT and invasion

Atypical protein kinase C ζ (PKCζ) forms an apico-basal polarity complex with Partitioning Defective (Pard) 3 and Pard6 to regulate normal epithelial cell apico-basolateral polarization. The dissociation of the PKCζ/Pard3/Pard6 complex is essential for the disassembly of the tight/adherens junction and epithelial-mesenchymal transition (EMT) that is critical for tumor spreading. Loss of cell polarity and epithelial organization is strongly correlated with malignancy and tumor progression in some other cancer types. However, it is unclear whether the PKCζ/Pard3/Pard6 complex plays a role in the progression of non-small-cell lung cancer (NSCLC). We found that hypoxia downregulated the PKCζ/Pard3/Pard6 complex, correlating with induction of lung cancer cell migration and invasion. Silencing of the PKCζ/Pard3/Pard6 polarity complex components induced lung cancer cell EMT, invasion, and colonization in vivo. Suppression of Pard3 was associated with altered expression of genes regulating wound healing, cell apoptosis/death and cell motility, and particularly upregulation of MAP3K1 and fibronectin which are known to contribute to lung cancer progression. Human lung adenocarcinoma tissues expressed less Pard6b and PKCζ than the adjacent normal tissues and in experimental mouse lung adenocarcinoma, the levels of Pard3 and PKCζ were also decreased. In addition, we showed that a methylation locus in the gene body of Pard3 is positively associated with the expression of Pard3 and that methylation of the Pard3 gene increased cellular sensitivity to carboplatin, a common chemotherapy drug. Suppression of Pard3 increased chemoresistance in lung cancer cells. Together, these results suggest that reduced expression of PKCζ/Pard3/Pard6 contributes to NSCLC EMT, invasion, and chemoresistance. © 2017 Elsevier Inc

Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees

We  present  a  new  methodology  for  handling  AIerrors by introducing weakly supervised AI error correctors witha prioriperformance guarantees. These AI correctors are auxiliarymaps  whose  role  is  to  moderate  the  decisions  of  some  previouslyconstructed underlying classifier by either approving or rejectingits  decisions.  The  rejection  of  a  decision  can  be  used  as  a  signalto  suggest  abstaining  from  making  a  decision.  A  key  technicalfocus  of  the  work  is  in  providing  performance  guarantees  forthese new AI correctors through bounds on the probabilities ofincorrect decisions. These bounds are distribution agnostic anddo not rely on assumptions on the data dimension. Our empiricalexample illustrates how the framework can be applied to improvethe performance of an image classifier in a challenging real-worldtask  where  training  data  are  scarce.</p

Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees

We  present  a  new  methodology  for  handling  AIerrors by introducing weakly supervised AI error correctors witha prioriperformance guarantees. These AI correctors are auxiliarymaps  whose  role  is  to  moderate  the  decisions  of  some  previouslyconstructed underlying classifier by either approving or rejectingits  decisions.  The  rejection  of  a  decision  can  be  used  as  a  signalto  suggest  abstaining  from  making  a  decision.  A  key  technicalfocus  of  the  work  is  in  providing  performance  guarantees  forthese new AI correctors through bounds on the probabilities ofincorrect decisions. These bounds are distribution agnostic anddo not rely on assumptions on the data dimension. Our empiricalexample illustrates how the framework can be applied to improvethe performance of an image classifier in a challenging real-worldtask  where  training  data  are  scarce.</p

Piglets cloned from induced pluripotent stem cells

Embryonic stem (ES) cells are powerful tools for generating genetically modified animals that can assist in advancing our knowledge of mammalian physiology and disease. Pigs provide outstanding models of human genetic diseases due to the striking similarities to human anatomy, physiology and genetics, but progress with porcine genetic engineering has been hampered by the lack of germline-competent pig ES cells. To overcome this limitation, genetically modified pigs have been produced using genetically modified somatic cells and nuclear transfer (NT). Yet, somatic cells exhibit limited proliferative capacity and have an extremely low frequency of homologous recombination compared to ES cells. Hence, only a few knockout pig models have been reported thus far using standard gene-targeting approaches

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

Due to the proliferation of biomedical imaging modalities, such as Photo-acoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc., massive amounts of data are generated on a daily basis. While massive biomedical data sets yield more information about pathologies, they also present new challenges of how to fully explore the data. Data fusion methods are a step forward towards a better understanding of data by bringing multiple data observations together to increase the consistency of the information. However, data generation is merely the first step, and there are many other factors involved in the fusion process like noise, missing data, data scarcity, and high dimensionality. In this paper, an overview of the advances in data preprocessing in biomedical data fusion is provided, along with insights stemming from new developments in the field

Diversity of reptile sex chromosome evolution revealed by cytogenetic and linked-read sequencing

Reptile sex determination is attracting much attention because the great diversity of sex-determination and dosage compensation mechanisms permits us to approach fundamental questions about mechanisms of sex chromosome turnover. Recent studies have made significant progress in better understanding diversity and conservation of reptile sex chromosomes, with however no reptile master sex determination genes identified. Here we describe an integrated genomics and cytogenetics pipeline, combining probes generated from the microdissected sex chromosomes with transcriptome and genome sequencing to explore the sex chromosome diversity in non-model Australian reptiles. We tested our pipeline on a turtle, two species of geckos, and a monitor lizard. Genes identified on sex chromosomes were compared to the chicken genome to identify homologous regions among the four species. We identified candidate sex determining genes within these regions, including conserved vertebrate sex-determining genes pdgfa, pdgfra amh and wt1, and demonstrated their testis or ovary-specific expression. All four species showed gene-by-gene rather than chromosome-wide dosage compensation. Our results imply that reptile sex chromosomes originated by independent acquisition of sex-determining genes on different autosomes, as well as translocations between different ancestral macro- and microchromosomes. We discuss the evolutionary drivers of the slow differentiation and turnover of reptile sex chromosomes. </p