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

The human papillomavirus type 11 and 16 E6 proteins modulate the cell-cycle regulator and transcription cofactor TRIP-Br1

AbstractThe genital human papillomaviruses (HPVs) are a taxonomic group including HPV types that preferentially cause genital and laryngeal warts (“low-risk types”), such as HPV-6 and HPV-11, or cancer of the cervix and its precursor lesions (“high-risk types”), such as HPV-16. The transforming processes induced by these viruses depend on the proteins E5, E6, and E7. Among these oncoproteins, the E6 protein stands out because it supports a particularly large number of functions and interactions with cellular proteins, some of which are specific for the carcinogenic HPVs, while others are shared among low- and high-risk HPVs. Here we report yeast two-hybrid screens with HPV-6 and -11 E6 proteins that identified TRIP-Br1 as a novel cellular target. TRIP-Br1 was recently detected by two research groups, which described two separate functions, namely that of a transcriptional integrator of the E2F1/DP1/RB cell-cycle regulatory pathway (and then named TRIP-Br1), and that of an antagonist of the cyclin-dependent kinase suppression of p16INK4a (and then named p34SEI-1). We observed that TRIP-Br1 interacts with low- and high-risk HPV E6 proteins in yeast, in vitro and in mammalian cell cultures. Transcription activation of a complex consisting of E2F1, DP1, and TRIP-Br1 was efficiently stimulated by both E6 proteins. TRIP-Br1 has an LLG E6 interaction motif, which contributed to the binding of E6 proteins. Apparently, E6 does not promote degradation of TRIP-Br1. Our observations imply that the cell-cycle promoting transcription factor E2F1/DP1 is dually targeted by HPV oncoproteins, namely (i) by interference of the E7 protein with repression by RB, and (ii) by the transcriptional cofactor function of the E6 protein. Our data reveal the natural context of the transcription activator function of E6, which has been predicted without knowledge of the E2F1/DP1/TRIP-Br/E6 complex by studying chimeric constructs, and add a function to the limited number of transforming properties shared by low- and high-risk HPVs

Carrageenan-based hydrogels for the controlled delivery of PDGF-BB in bone tissue engineering applications

One of the major drawbacks found in most bone tissue engineering approaches developed so far consists in the lack of strategies to promote vascularisation. Some studies have addressed different issues that may enhance vascularisation in tissue engineered constructs, most of them involving the use of growth factors (GFs) that are involved in the restitution of the vascularity in a damaged zone. The use of sustained delivery systems might also play an important role in the re-establishment of angiogenesis. In this study, !-carrageenan, a naturally occurring polymer, was used to develop hydrogel beads with the ability to incorporate GFs with the purpose of establishing an effective angiogenesis mechanism. Some processing parameters were studied and their influence on the final bead properties was evaluated. Platelet derived growth factor (PDGF-BB) was selected as the angiogenic factor to incorporate in the developed beads, and the results demonstrate the achievement of an efficient encapsulation and controlled release profile matching those usually required for the development of a fully functional vascular network. In general, the obtained results demonstrate the potential of these systems for bone tissue engineering applications.This work was supported by the European NoE EXPERTISSUES (NMP3-CT-2004-500283), the European STREP HIPPOCRATES (NMP3-CT-2003-505758), and by the Portuguese Foundation for Science and Technology (FCT) through the project PTDC/FIS/68517/2006 and through the V. Espirito Santo's Ph.D. grant (SFRH/BD/39486/2007)

Manipulating Protein Conformations By Single-molecule Afm-fret Nanoscopy

Combining atomic force microscopy and fluorescence resonance energy transfer spectroscopy (AFM-FRET), we have developed a single-molecule AFM-FRET nanoscopy approach capable of effectively pinpointing and mechanically manipulating a targeted dye-labeled single protein in a large sampling area and simultaneously monitoring the conformational changes of the targeted protein by recording single-molecule FRET time trajectories. We have further demonstrated an application of using this nanoscopy on manipulation of single-molecule protein conformation and simultaneous single-molecule FRET measurement of a Cy3-Cy5-labeled kinase enzyme, HPPK (6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase). By analyzing time-resolved FRET trajectories and correlated AFM force pulling curves of the targeted single-molecule enzyme, we are able to observe the protein conformational changes of a specific coordination by AFM mechanic force pulling

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments

Many suspensions, many problems: a review of self-suspending tasks in real-time systems

In general computing systems, a job (process/task) may suspend itself whilst it is waiting for some activity to complete, e.g., an accelerator to return data. In real-time systems, such self-suspension can cause substantial performance/schedulability degradation. This observation, first made in 1988, has led to the investigation of the impact of self-suspension on timing predictability, and many relevant results have been published since. Unfortunately, as it has recently come to light, a number of the existing results are flawed. To provide a correct platform on which future research can be built, this paper reviews the state of the art in the design and analysis of scheduling algorithms and schedulability tests for self-suspending tasks in real-time systems. We provide (1) a systematic description of how self-suspending tasks can be handled in both soft and hard real-time systems; (2) an explanation of the existing misconceptions and their potential remedies; (3) an assessment of the influence of such flawed analyses on partitioned multiprocessor fixed-priority scheduling when tasks synchronize access to shared resources; and (4) a discussion of the computational complexity of analyses for different self-suspension task models

Reliability of functional magnetic resonance imaging activation during working memory in a multi-site study: Analysis from the North American Prodrome Longitudinal Study

Multi-site neuroimaging studies offer an efficient means to study brain functioning in large samples of individuals with rare conditions; however, they present new challenges given that aggregating data across sites introduces additional variability into measures of interest. Assessing the reliability of brain activation across study sites and comparing statistical methods for pooling functional data is critical to ensuring the validity of aggregating data across sites. The current study used two samples of healthy individuals to assess the feasibility and reliability of aggregating multi-site functional magnetic resonance imaging (fMRI) data from a Sternberg-style verbal working memory task. Participants were recruited as part of the North American Prodrome Longitudinal Study (NAPLS), which comprises eight fMRI scanning sites across the United States and Canada. In the first study sample (n = 8), one participant from each home site traveled to each of the sites and was scanned while completing the task on two consecutive days. Reliability was examined using generalizability theory. Results indicated that blood oxygen level-dependent (BOLD) signal was reproducible across sites and was highly reliable, or generalizable, across scanning sites and testing days for core working memory ROIs (generalizability ICCs = 0.81 for left dorsolateral prefrontal cortex, 0.95 for left superior parietal cortex). In the second study sample (n = 154), two statistical methods for aggregating fMRI data across sites for all healthy individuals recruited as control participants in the NAPLS study were compared. Control participants were scanned on one occasion at the site from which they were recruited. Results from the image-based meta-analysis (IBMA) method and mixed effects model with site covariance method both showed robust activation in expected regions (i.e. dorsolateral prefrontal cortex, anterior cingulate cortex, supplementary motor cortex, superior parietal cortex, inferior temporal cortex, cerebellum, thalamus, basal ganglia). Quantification of the similarity of group maps from these methods confirmed a very high (96%) degree of spatial overlap in results. Thus, brain activation during working memory function was reliable across the NAPLS sites and both the IBMA and mixed effects model with site covariance methods appear to be valid approaches for aggregating data across sites. These findings indicate that multi-site functional neuroimaging can offer a reliable means to increase power and generalizability of results when investigating brain function in rare populations and support the multi-site investigation of working memory function in the NAPLS study, in particular

Association of Thalamic Dysconnectivity and Conversion to Psychosis in Youth and Young Adults at Elevated Clinical Risk

Severe neuropsychiatric conditions, such as schizophrenia, affect distributed neural computations. One candidate system profoundly altered in chronic schizophrenia involves the thalamocortical networks. It is widely acknowledged that schizophrenia is a neurodevelopmental disorder that likely affects the brain before onset of clinical symptoms. However, no investigation has tested whether thalamocortical connectivity is altered in individuals at risk for psychosis or whether this pattern is more severe in individuals who later develop full-blown illness