Keratin 5 knockout mice reveal plasticity of keratin expression in the corneal epithelium

We have recently demonstrated that the keratin K3 gene, which is active in the suprabasal human corneal epithelium, is missing in the genome of the mouse. We show that a normal K3 gene exists in a wide variety of mammals while in rodents the gene is converted to a pseudogene with a very strong sequence drift. The availability of K5(-/-) mice provides a unique opportunity to investigate type-specific keratin function during corneal differentiation in the absence of both K5 and K3. Here, we report that the deletion of K5, which in wild-type mice forms a cytoskeleton with K12, does neither cause keratin aggregation nor cytolysis in the cornea. This is due to the induction of K4 in corneal epithelial cells, normally restricted to corneal stem stem cells residing in the limbus. Using a combination of antibodies and RT-PCR, we identified additional keratins expressed in the mouse cornea including K23 which was previously thought to be specific for pancreatic carcinomas. This reflects an unexpected complexity of keratin expression in the cornea. Our data suggest that in the absence of mechanical stress, corneal differentiation does not depend on distinct keratin pairs, supporting a concept of functional redundancy, at least for certain keratins

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

On the Effectiveness of Image Rotation for Open Set Domain Adaptation

Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source. To avoid negative transfer, OSDA can be tackled by first separating the known/unknown target samples and then aligning known target samples with the source data. We propose a novel method to addresses both these problems using the self-supervised task of rotation recognition. Moreover, we assess the performance with a new open set metric that properly balances the contribution of recognizing the known classes and rejecting the unknown samples. Comparative experiments with existing OSDA methods on the standard Office-31 and Office-Home benchmarks show that: (i) our method outperforms its competitors, (ii) reproducibility for this field is a crucial issue to tackle, (iii) our metric provides a reliable tool to allow fair open set evaluation.Comment: accepted at ECCV 202

Protein disulphide isomerase-assisted functionalization of proteinaceous substrates

Protein disulphide isomerase (PDI) is an enzyme that catalyzes thiol-disulphide exchange reactions among a broad spectrum of substrates, including proteins and low-molecular thiols and disulphides. As the first protein-folding catalyst reported, the study of PDI has mainly involved the correct folding of several cysteine-containing proteins. Its application on the functionalization of protein-based materials has not been extensively reported. Herein, we review the applications of PDI on the modification of proteinaceous substrates and discuss its future potential. The mechanism involved in PDI functionalization of fibrous protein substrates is discussed in detail. These approaches allow innovative applications in textile dyeing and finishing, medical textiles, controlled drug delivery systems and hair or skin care products.We thank to FCT 'Fundacao para a Ciencia e Tecnologia' (scholarship SFRH/BD/38363/2007) for providing Margarida Fernandes the grant for PhD studies

C-type lectin-like domains in Fugu rubripes

BACKGROUND: Members of the C-type lectin domain (CTLD) superfamily are metazoan proteins functionally important in glycoprotein metabolism, mechanisms of multicellular integration and immunity. Three genome-level studies on human, C. elegans and D. melanogaster reported previously demonstrated almost complete divergence among invertebrate and mammalian families of CTLD-containing proteins (CTLDcps). RESULTS: We have performed an analysis of CTLD family composition in Fugu rubripes using the draft genome sequence. The results show that all but two groups of CTLDcps identified in mammals are also found in fish, and that most of the groups have the same members as in mammals. We failed to detect representatives for CTLD groups V (NK cell receptors) and VII (lithostathine), while the DC-SIGN subgroup of group II is overrepresented in Fugu. Several new CTLD-containing genes, highly conserved between Fugu and human, were discovered using the Fugu genome sequence as a reference, including a CSPG family member and an SCP-domain-containing soluble protein. A distinct group of soluble dual-CTLD proteins has been identified, which may be the first reported CTLDcp group shared by invertebrates and vertebrates. We show that CTLDcp-encoding genes are selectively duplicated in Fugu, in a manner that suggests an ancient large-scale duplication event. We have verified 32 gene structures and predicted 63 new ones, and make our annotations available through a distributed annotation system (DAS) server and their sequences as additional files with this paper. CONCLUSIONS: The vertebrate CTLDcp family was essentially formed early in vertebrate evolution and is completely different from the invertebrate families. Comparison of fish and mammalian genomes revealed three groups of CTLDcps and several new members of the known groups, which are highly conserved between fish and mammals, but were not identified in the study using only mammalian genomes. Despite limitations of the draft sequence, the Fugu rubripes genome is a powerful instrument for gene discovery and vertebrate evolutionary analysis. The composition of the CTLDcp superfamily in fish and mammals suggests that large-scale duplication events played an important role in the evolution of vertebrates