Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye

We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-tracking signal. Compared to prior work, the network attains a lower error rate by one order of magnitude and is faster by two orders of magnitude: it identifies users accurately within seconds

Structural hierarchies define toughness and defect-tolerance despite simple and mechanically inferior brittle building blocks

Mineralized biological materials such as bone, sea sponges or diatoms provide load-bearing and armor functions and universally feature structural hierarchies from nano to macro. Here we report a systematic investigation of the effect of hierarchical structures on toughness and defect-tolerance based on a single and mechanically inferior brittle base material, silica, using a bottom-up approach rooted in atomistic modeling. Our analysis reveals drastic changes in the material crack-propagation resistance (R-curve) solely due to the introduction of hierarchical structures that also result in a vastly increased toughness and defect-tolerance, enabling stable crack propagation over an extensive range of crack sizes. Over a range of up to four hierarchy levels, we find an exponential increase in the defect-tolerance approaching hundred micrometers without introducing additional mechanisms or materials. This presents a significant departure from the defect-tolerance of the base material, silica, which is brittle and highly sensitive even to extremely small nanometer-scale defects

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person

New fossils of Australopithecus sediba reveal a nearly complete lower back

Abstract: Adaptations of the lower back to bipedalism are frequently discussed but infrequently demonstrated in early fossil hominins. Newly discovered lumbar vertebrae contribute to a near-complete lower back of Malapa Hominin 2 (MH2), offering additional insights into posture and locomotion in Australopithecus sediba. We show that MH2 demonstrates a lower back consistent with human-like lumbar lordosis and other adaptations to bipedalism, including an increase in the width of intervertebral articular facets from the upper to lower lumbar column (“pyramidal configuration”). This contrasts with recent work on lordosis in fossil hominins, where MH2 was argued to demonstrate no appreciable lordosis (“hypolordosis”) similar to Neandertals. Our three-dimensional geometric morphometric (3D GM) analyses show that MH2’s nearly complete middle lumbar vertebra is human-like in shape but bears large, cranially-directed transverse processes, implying powerful trunk musculature. We interpret this combination of features to indicate that A. sediba used its lower back in both human-like bipedalism and ape-like arboreal positional behaviors, as previously suggested based on multiple lines of evidence from other parts of the skeleton and reconstructed paleobiology of A. sediba

Rough Fibrils Provide a Toughening Mechanism in Biological Fibers

Spider silk is a fascinating natural composite material. Its combination of strength and toughness is unrivalled in nature, and as a result, it has gained considerable interest from the medical, physics, and materials communities. Most of this attention has focused on the one to tens of nanometer scale: predominantly the primary (peptide sequences) and secondary (β sheets, helices, and amorphous domains) structure, with some insights into tertiary structure (the arrangement of these secondary structures) to describe the origins of the mechanical and biological performance. Starting with spider silk, and relating our findings to collagen fibrils, we describe toughening mechanisms at the hundreds of nanometer scale, namely, the fibril morphology and its consequences for mechanical behavior and the dissipation of energy. Under normal conditions, this morphology creates a nonslip fibril kinematics, restricting shearing between fibrils, yet allowing controlled local slipping under high shear stress, dissipating energy without bulk fracturing. This mechanism provides a relatively simple target for biomimicry and, thus, can potentially be used to increase fracture resistance in synthetic materials

Molecular mechanics of mineralized collagen fibrils in bone

Bone is a natural composite of collagen protein and the mineral hydroxyapatite. The structure of bone is known to be important to its load-bearing characteristics, but relatively little is known about this structure or the mechanism that govern deformation at the molecular scale. Here we perform full-atomistic calculations of the three-dimensional molecular structure of a mineralized collagen protein matrix to try to better understand its mechanical characteristics under tensile loading at various mineral densities. We find that as the mineral density increases, the tensile modulus of the network increases monotonically and well beyond that of pure collagen fibrils. Our results suggest that the mineral crystals within this network bears up to four times the stress of the collagen fibrils, whereas the collagen is predominantly responsible for the material’s deformation response. These findings reveal the mechanism by which bone is able to achieve superior energy dissipation and fracture resistance characteristics beyond its individual constituents.United States. Office of Naval Research (N000141010562)United States. Army Research Office (W991NF-09-1-0541)United States. Army Research Office (W911NF-10-1-0127)National Science Foundation (U.S.) (CMMI-0642545

Identification of germline alterations of the mad homology 2 domain of SMAD3 and SMAD4 from the Ontario site of the breast cancer family registry (CFR)

Abstract Introduction A common feature of neoplastic cells is that mutations in SMADs can contribute to the loss of sensitivity to the anti-tumor effects of transforming growth factor-β (TGF-β). However, germline mutation analysis of SMAD3 and SMAD4, the principle substrates of the TGF-β signaling pathway, has not yet been conducted in breast cancer. Thus, it is currently unknown whether germline SMAD3 and SMAD4 mutations are involved in breast cancer predisposition. Methods We performed mutation analysis of the highly conserved mad-homology 2 (MH2) domains for both genes in genomic DNA from 408 non-BRCA1/BRCA2 breast cancer cases and 710 population controls recruited by the Ontario site of the breast cancer family registry (CFR) using denaturing high-performance liquid chromatography (DHPLC) and direct DNA sequencing. The results were interpreted in several ways. First, we adapted nucleotide diversity analysis to quantitatively assess whether the frequency of alterations differ between the two genes. Next, in silico tools were used to predict variants' effect on domain function and mRNA splicing. Finally, 37 cases or controls harboring alterations were tested for aberrant splicing using reverse-transcription polymerase chain reaction (PCR) and real-time PCR statistical comparison of germline expressions by non-parametric Mann-Whitney test of independent samples. Results We identified 27 variants including 2 novel SMAD4 coding variants c.1350G > A (p.Gln450Gln), and c.1701A > G (p.Ile525Val). There were no inactivating mutations even though c.1350G > A was predicted to affect exonic splicing enhancers. However, several additional findings were of note: 1) nucleotide diversity estimate for SMAD3 but not SMAD4 indicated that coding variants of the MH2 domain were more infrequent than expected; 2) in breast cancer cases SMAD3 was significantly over-expressed relative to controls (P A was associated with elevated germline expression (> 5-fold); 3) separate analysis using tissue expression data showed statistically significant over-expression of SMAD3 and SMAD4 in breast carcinomas. Conclusions This study shows that inactivating germline alterations in SMAD3 and SMAD4 are rare, suggesting a limited role in driving tumorigenesis. Nevertheless, aberrant germline expressions of SMAD3 and SMAD4 may be more common in breast cancer than previously suspected and offer novel insight into their roles in predisposition and/or progression of breast cancer

Downregulation of uPAR and Cathepsin B Induces Apoptosis via Regulation of Bcl-2 and Bax and Inhibition of the PI3K/Akt Pathway in Gliomas

Glioma is the most commonly diagnosed primary brain tumor and is characterized by invasive and infiltrative behavior. uPAR and cathepsin B are known to be overexpressed in high-grade gliomas and are strongly correlated with invasive cancer phenotypes.In the present study, we observed that simultaneous downregulation of uPAR and cathepsin B induces upregulation of some pro-apoptotic genes and suppression of anti-apoptotic genes in human glioma cells. uPAR and cathepsin B (pCU)-downregulated cells exhibited decreases in the Bcl-2/Bax ratio and initiated the collapse of mitochondrial membrane potential. We also observed that the broad caspase inhibitor, Z-Asp-2, 6-dichlorobenzoylmethylketone rescued pCU-induced apoptosis in U251 cells but not in 5310 cells. Immunoblot analysis of caspase-9 immunoprecipitates for Apaf-1 showed that uPAR and cathepsin B knockdown activated apoptosome complex formation in U251 cells. Downregulation of uPAR and cathepsin B also retarded nuclear translocation and interfered with DNA binding activity of CREB in both U251 and 5310 cells. Further western blotting analysis demonstrated that downregulation of uPAR and cathepsin B significantly decreased expression of the signaling molecules p-PDGFR-β, p-PI3K and p-Akt. An increase in the number of TUNEL-positive cells, increased Bax expression, and decreased Bcl-2 expression in nude mice brain tumor sections and brain tissue lysates confirm our in vitro results.In conclusion, RNAi-mediated downregulation of uPAR and cathepsin B initiates caspase-dependent mitochondrial apoptosis in U251 cells and caspase-independent mitochondrial apoptosis in 5310 cells. Thus, targeting uPAR and cathepsin B-mediated signaling using siRNA may serve as a novel therapeutic strategy for the treatment of gliomas