Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. Given a suitable training dataset, we utilize deep learning techniques to address the classification problem. Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks. The first "patch-wise" network acts as an auto-encoder that extracts the most salient features of image patches while the second "image-wise" network performs classification of the whole image. The first network is pre-trained and aimed at extracting local information while the second network obtains global information of an input image. We trained the networks using the ICIAR 2018 grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method yields 95 % accuracy on the validation set compared to previously reported 77 % accuracy rates in the literature. Our code is publicly available at https://github.com/ImagingLab/ICIAR2018Comment: 10 pages, 5 figures, ICIAR 2018 conferenc

Further advantages of data augmentation on convolutional neural networks

Data augmentation is a popular technique largely used to enhance the training of convolutional neural networks. Although many of its benefits are well known by deep learning researchers and practitioners, its implicit regularization effects, as compared to popular explicit regularization techniques, such as weight decay and dropout, remain largely unstudied. As a matter of fact, convolutional neural networks for image object classification are typically trained with both data augmentation and explicit regularization, assuming the benefits of all techniques are complementary. In this paper, we systematically analyze these techniques through ablation studies of different network architectures trained with different amounts of training data. Our results unveil a largely ignored advantage of data augmentation: networks trained with just data augmentation more easily adapt to different architectures and amount of training data, as opposed to weight decay and dropout, which require specific fine-tuning of their hyperparameters.Comment: Preprint of the manuscript accepted for presentation at the International Conference on Artificial Neural Networks (ICANN) 2018. Best Paper Awar

Deep Learning to Analyze RNA-Seq Gene Expression Data

Deep learning models are currently being applied in several areas with great success. However, their application for the analysis of high-throughput sequencing data remains a challenge for the research community due to the fact that this family of models are known to work very well in big datasets with lots of samples available, just the opposite scenario typically found in biomedical areas. In this work, a first approximation on the use of deep learning for the analysis of RNA-Seq gene expression profiles data is provided. Three public cancer-related databases are analyzed using a regularized linear model (standard LASSO) as baseline model, and two deep learning models that differ on the feature selection technique used prior to the application of a deep neural net model. The results indicate that a straightforward application of deep nets implementations available in public scientific tools and under the conditions described within this work is not enough to outperform simpler models like LASSO. Therefore, smarter and more complex ways that incorporate prior biological knowledge into the estimation procedure of deep learning models may be necessary in order to obtain better results in terms of predictive performance.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

BART Inhibits Pancreatic Cancer Cell Invasion by PKCα Inactivation through Binding to ANX7

A novel function for the binder of Arl two (BART) molecule in pancreatic cancer cells is reported. BART inhibits invasiveness of pancreatic cancer cells through binding to a Ca2+-dependent, phosphorylated, guanosine triphosphatase (GTPase) membrane fusion protein, annexin7 (ANX7). A tumor suppressor function for ANX7 was previously reported based on its prognostic role in human cancers and the cancer-prone mouse phenotype ANX7(+/−). Further investigation demonstrated that the BART–ANX7 complex is transported toward cell protrusions in migrating cells when BART supports the binding of ANX7 to the protein kinase C (PKC) isoform PKCα. Recent evidence has suggested that phosphorylation of ANX7 by PKC significantly potentiates ANX7-induced fusion of phospholipid vesicles; however, the current data suggest that the BART–ANX7 complex reduces PKCα activity. Knocking down endogenous BART and ANX7 increases activity of PKCα, and specific inhibitors of PKCα significantly abrogate invasiveness induced by BART and ANX7 knockdown. These results imply that BART contributes to regulating PKCα activity through binding to ANX7, thereby affecting the invasiveness of pancreatic cancer cells. Thus, it is possible that BART and ANX7 can distinctly regulate the downstream signaling of PKCα that is potentially relevant to cell invasion by acting as anti-invasive molecules

Evolutionary Toggling of Vpx/Vpr Specificity Results in Divergent Recognition of the Restriction Factor SAMHD1

SAMHD1 is a host restriction factor that blocks the ability of lentiviruses such as HIV-1 to undergo reverse transcription in myeloid cells and resting T-cells. This restriction is alleviated by expression of the lentiviral accessory proteins Vpx and Vpr (Vpx/Vpr), which target SAMHD1 for proteasome-mediated degradation. However, the precise determinants within SAMHD1 for recognition by Vpx/Vpr remain unclear. Here we show that evolution of Vpx/Vpr in primate lentiviruses has caused the interface between SAMHD1 and Vpx/Vpr to alter during primate lentiviral evolution. Using multiple HIV-2 and SIV Vpx proteins, we show that Vpx from the HIV-2 and SIVmac lineage, but not Vpx from the SIVmnd2 and SIVrcm lineage, require the C-terminus of SAMHD1 for interaction, ubiquitylation, and degradation. On the other hand, the N-terminus of SAMHD1 governs interactions with Vpx from SIVmnd2 and SIVrcm, but has little effect on Vpx from HIV-2 and SIVmac. Furthermore, we show here that this difference in SAMHD1 recognition is evolutionarily dynamic, with the importance of the N- and C-terminus for interaction of SAMHD1 with Vpx and Vpr toggling during lentiviral evolution. We present a model to explain how the head-to-tail conformation of SAMHD1 proteins favors toggling of the interaction sites by Vpx/Vpr during this virus-host arms race. Such drastic functional divergence within a lentiviral protein highlights a novel plasticity in the evolutionary dynamics of viral antagonists for restriction factors during lentiviral adaptation to its hosts. © 2013 Fregoso et al

Entrepreneurs’ age, institutions, and social value creation goals: a multi-country study

This study explores the relationship between an entrepreneur's age and his/her social value creation goals. Building on the lifespan developmental psychology literature and institutional theory, we hypothesize a U-shaped relationship between entrepreneurs’ age and their choice to create social value through their ventures, such that younger and older entrepreneurs create more social value with their businesses while middle age entrepreneurs are relatively more economically and less socially oriented with their ventures. We further hypothesize that the quality of a country’s formal institutions in terms of economic, social, and political freedom steepen the U-shaped relationship between entrepreneurs’ age and their choice to pursue social value creation as supportive institutional environments allow entrepreneurs to follow their age-based preferences. We confirm our predictions using multilevel mixed-effects linear regressions on a sample of over 15,000 entrepreneurs (aged between 18 and 64 years) in 45 countries from Global Entrepreneurship Monitor data. The findings are robust to several alternative specifications. Based on our findings, we discuss implications for theory and practice, and we propose future research directions

Employers’ Perception on the Antecedents of Graduate Employability for the Information Technology Sector

This chapter aims to analyze the perceptions of the employers in the Information Technology (IT) sector in India on the antecedents of graduate employability. With an increased emphasis on Organizational flexibility in today’s volatile and complex Business environment, the employability of the workforce has gained crucial significance. Flexibility has been acknowledged as a predictor of Organizational performance (Sushil, Global J Flex Syst Manag 16(4):309–311, 2015) and its Strategic driver (Sharma et al., Global J Flex Syst Manag 11(3):51–68, 2010). Flexible strategies and business plan often demand the need to scale up the quality of manpower or shift the required skill set to swiftly adapt to the Market changes accordingly. This Flexibility is not confined to the quantity of manpower only but also encompasses the quality of skills deployed by the manpower (Srivastava, Global J Flex Syst Manag 17(1):105–108, 2016). Therefore, it is imperative for the potential Job seeker to understand and continuously adapt to the changing knowledge and skill requirements of the employers to develop and maintain their employability. The employers in this dynamic sector demand a range of knowledge, skills, and other attributes from potential job seekers. However, the graduates passing out of Higher Education Institutions fail to meet these expectations of the employers. Therefore, the sector is struggling with the challenges of talent crunch and qualitative demand–Demand–supply mismatch of manpower. The identification of factors that influence graduate employability is based on literature review. This chapter is empirical and examines the perceptions of the employers on the factors that impact employability and validates the association between the research constructs. Opinion surveys are used to elicit responses from a sample of 236 respondents, i.e., Technical/HR personnel at the middle-level/upper middle-level management positions spanning across 71 reputed IT companies in India. These respondents are actively involved in the staffing of graduates seeking Technical jobs in IT sector. The perception of these employers has been investigated using bivariate and multivariate analysis techniques. The key insights drawn in this chapter enable potential job seekers to clearly understand the employer demands in the IT sector and equip themselves with the required knowledge and skills. This also contributes to enhancing the manpower Flexibility in organizations. The chapter has significant implications for the policy-makers and key stakeholders to bridge the Employability gap in this sector

Elicitation of Neutralizing Antibodies Directed against CD4-Induced Epitope(s) Using a CD4 Mimetic Cross-Linked to a HIV-1 Envelope Glycoprotein

The identification of HIV-1 envelope glycoprotein (Env) structures that can generate broadly neutralizing antibodies (BNAbs) is pivotal to the development of a successful vaccine against HIV-1 aimed at eliciting effective humoral immune responses. To that end, the production of novel Env structure(s) that might induce BNAbs by presentation of conserved epitopes, which are otherwise occluded, is critical. Here, we focus on a structure that stabilizes Env in a conformation representative of its primary (CD4) receptor-bound state, thereby exposing highly conserved “CD4 induced” (CD4i) epitope(s) known to be important for co-receptor binding and subsequent virus infection. A CD4-mimetic miniprotein, miniCD4 (M64U1-SH), was produced and covalently complexed to recombinant, trimeric gp140 envelope glycoprotein (gp140) using site-specific disulfide linkages. The resulting gp140-miniCD4 (gp140-S-S-M64U1) complex was recognized by CD4i antibodies and the HIV-1 co-receptor, CCR5. The gp140-miniCD4 complex elicited the highest titers of CD4i binding antibodies as well as enhanced neutralizing antibodies against Tier 1 viruses as compared to gp140 protein alone following immunization of rabbits. Neutralization against HIV-27312/V434M and additional serum mapping confirm the specific elicitation of antibodies directed to the CD4i epitope(s). These results demonstrate the utility of structure-based approach in improving immunogenic response against specific region, such as the CD4i epitope(s) here, and its potential role in vaccine application

Sequence Homology at the Breakpoint and Clinical Phenotype of Mitochondrial DNA Deletion Syndromes

Mitochondrial DNA (mtDNA) deletions are a common cause of mitochondrial disorders. Large mtDNA deletions can lead to a broad spectrum of clinical features with different age of onset, ranging from mild mitochondrial myopathies (MM), progressive external ophthalmoplegia (PEO), and Kearns-Sayre syndrome (KSS), to severe Pearson syndrome. The aim of this study is to investigate the molecular signatures surrounding the deletion breakpoints and their association with the clinical phenotype and age at onset. MtDNA deletions in 67 patients were characterized using array comparative genomic hybridization (aCGH) followed by PCR-sequencing of the deletion junctions. Sequence homology including both perfect and imperfect short repeats flanking the deletion regions were analyzed and correlated with clinical features and patients' age group. In all age groups, there was a significant increase in sequence homology flanking the deletion compared to mtDNA background. The youngest patient group (<6 years old) showed a diffused pattern of deletion distribution in size and locations, with a significantly lower sequence homology flanking the deletion, and the highest percentage of deletion mutant heteroplasmy. The older age groups showed rather discrete pattern of deletions with 44% of all patients over 6 years old carrying the most common 5 kb mtDNA deletion, which was found mostly in muscle specimens (22/41). Only 15% (3/20) of the young patients (<6 years old) carry the 5 kb common deletion, which is usually present in blood rather than muscle. This group of patients predominantly (16 out of 17) exhibit multisystem disorder and/or Pearson syndrome, while older patients had predominantly neuromuscular manifestations including KSS, PEO, and MM. In conclusion, sequence homology at the deletion flanking regions is a consistent feature of mtDNA deletions. Decreased levels of sequence homology and increased levels of deletion mutant heteroplasmy appear to correlate with earlier onset and more severe disease with multisystem involvement