Pairwise Confusion for Fine-Grained Visual Classification

Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance. In this work, we address this problem using a novel optimization procedure for the end-to-end neural network training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces overfitting by intentionally {introducing confusion} in the activations. With PC regularization, we obtain state-of-the-art performance on six of the most widely-used FGVC datasets and demonstrate improved localization ability. {PC} is easy to implement, does not need excessive hyperparameter tuning during training, and does not add significant overhead during test time.Comment: Camera-Ready version for ECCV 201

Sol-gel based materials for biomedical applications

Sol-gel chemistry offers a flexible approach to obtaining a diverse range of materials. It allows differing chemistries to be achieved as well as offering the ability to produce a wide range of nano-/micro-structures. The paper commences with a generalized description of the various sol-gel methods available and how these chemistries control the bulk properties of the end products. Following this, a more detailed description of the biomedical areas where sol-gel materials have been explored and found to hold significant potential. One of the interesting fields that has been developed recently relates to hybrid materials that utilize sol-gel chemistry to achieve unusual composite properties. Another intriguing feature of sol-gels is the unusual morphologies that are achievable at the micro- and nano-scale. Subsequently the ability to control pore chemistry at a number of different length scales and geometries has proven to be a fruitful area of exploitation, that provides excellent bioactivity and attracts cellular responses as well as enables the entrapment of biologically active molecules and their controllable release for therapeutic action. The approaches of fine-tuning surface chemistry and the combination with other nanomaterials have also enabled targeting of specific cell and tissue types for drug delivery with imaging capacity

Direct observation of delithiation as the origin of analog memristance in LixNbO2

The discovery of analog LixNbO2 memristors revealed a promising new memristive mechanism wherein the diffusion of Li+ rather than O2- ions enables precise control of the resistive states. However, directly correlating lithium concentration with changes to the electronic structure in active layers remains a challenge and is required to truly understand the underlying physics. Chemically delithiated single crystals of LiNbO2 present a model system for correlating lithium variation with spectroscopic signatures from operando soft x-ray spectroscopy studies of device active layers. Using electronic structure modeling of the x-ray spectroscopy of LixNbO2 single crystals, we demonstrate that the intrinsic memristive behavior in LixNbO2 active layers results from field-induced degenerate p-type doping. We show that electrical operation of LixNbO2-based memristors is viable even at marginal Li deficiency and that the analog memristive switching occurs well before the system is fully metallic. This study serves as a benchmark for material synthesis and characterization of future LixNbO2-based memristor devices and suggests that valence change switching is a scalable alternative that circumvents the electroforming typically required for filamentary-based memristors

Illuminating glycoscience: synthetic strategies for FRET-enabled carbohydrate active enzyme probes

Carbohydrates are synthesised, refined and degraded by carbohydrate active enzymes. FRET is emerging as a powerful tool to monitor and quantify their activity as well as to test inhibitors as new drug candidates and monitor disease

Multi-stage machine learning model for hierarchical tie valence prediction

This is the final version. Available from the Association for Computing Machinery via the DOI in this record. Data availability: Due to our non-disclosure agreement with the organization and our Institutional Review Board data management protocol, the raw data cannot be shared.Individuals interacting in organizational settings involving varying levels of formal hierarchy naturally form a complex network of social ties having different tie valences (e.g., positive and negative connections). Social ties critically affect employees' satisfaction, behaviors, cognition, and outcomes - yet identifying them solely through survey data is challenging because of the large size of some organizations or the often hidden nature of these ties and their valences. We present a novel deep learning model encompassing NLP and graph neural network techniques that identifies positive and negative ties in a hierarchical network. The proposed model uses human resource attributes as node information and web-logged work conversation data as link information. Our findings suggest that the presence of conversation data improves the tie valence classification by 8.91% compared to employing user attributes alone. This gain came from accurately distinguishing positive ties, particularly for male, non-minority, and older employee groups. We also show a substantial difference in conversation patterns for positive and negative ties with positive ties being associated with more messages exchanged on weekends, and lower use of words related to anger and sadness. These findings have broad implications for facilitating collaboration and managing conflict within organizational and other social networks.Institute for Basic Sciences (IBS), Republic of KoreaNational Research Foundation of Korea (NRF

Epithelial Barrier Integrity Profiling: Combined Approach Using Cellular Junctional Complex Imaging and Transepithelial Electrical Resistance

A core aspect of epithelial cell function is barrier integrity. A loss of barrier integrity is a feature of a number of respiratory diseases, including asthma, allergic rhinitis, and chronic obstructive pulmonary disease. Restoration of barrier integrity is a target for respiratory disease drug discovery. Traditional methods for assessing barrier integrity have their limitations. Transepithelial electrical resistance (TEER) and dextran permeability methods can give poor in vitro assay robustness. Traditional junctional complex imaging approaches are labor-intensive and tend to be qualitative but not quantitative. To provide a robust and quantitative assessment of barrier integrity, high-content imaging of junctional complexes was combined with TEER. A scalable immunofluorescent high-content imaging technique, with automated quantification of junctional complex proteins zonula occludens-1 and occludin, was established in 3D pseudostratified primary human bronchial epithelial cells cultured at an air–liquid interface. Ionic permeability was measured using TEER on the same culture wells. The improvements to current technologies include the design of a novel 24-well holder to enable scalable in situ confocal cell imaging without Transwell membrane excision, the development of image analysis pipelines to quantify in-focus junctional complex structures in each plane of a Z stack, and the enhancement of the TEER data analysis process to enable statistical evaluation of treatment effects on barrier integrity. This novel approach was validated by demonstrating measurable changes in barrier integrity in cells grown under conditions known to perturb epithelial cell function

Molecular alterations that drive breast cancer metastasis to bone.

Epithelial cancers including breast and prostate commonly progress to form incurable bone metastases. For this to occur, cancer cells must adapt their phenotype and behaviour to enable detachment from the primary tumour, invasion into the vasculature, and homing to and subsequent colonisation of bone. It is widely accepted that the metastatic process is driven by the transformation of cancer cells from a sessile epithelial to a motile mesenchymal phenotype through epithelial-mesenchymal transition (EMT). Dissemination of these motile cells into the circulation provides the conduit for cells to metastasise to distant organs. However, accumulating evidence suggests that EMT is not sufficient for metastasis to occur and that specific tissue-homing factors are required for tumour cells to lodge and grow in bone. Once tumour cells are disseminated in the bone environment, they can revert into an epithelial phenotype through the reverse process of mesenchymal-epithelial transition (MET) and form secondary tumours. In this review, we describe the molecular alterations undertaken by breast cancer cells at each stage of the metastatic cascade and discuss how these changes facilitate bone metastasis

A statistical framework for integrating two microarray data sets in differential expression analysis

<p>Abstract</p> <p>Background</p> <p>Different microarray data sets can be collected for studying the same or similar diseases. We expect to achieve a more efficient analysis of differential expression if an efficient statistical method can be developed for integrating different microarray data sets. Although many statistical methods have been proposed for data integration, the genome-wide concordance of different data sets has not been well considered in the analysis.</p> <p>Results</p> <p>Before considering data integration, it is necessary to evaluate the genome-wide concordance so that misleading results can be avoided. Based on the test results, different subsequent actions are suggested. The evaluation of genome-wide concordance and the data integration can be achieved based on the normal distribution based mixture models.</p> <p>Conclusion</p> <p>The results from our simulation study suggest that misleading results can be generated if the genome-wide concordance issue is not appropriately considered. Our method provides a rigorous parametric solution. The results also show that our method is robust to certain model misspecification and is practically useful for the integrative analysis of differential expression.</p