Transitioning between Convolutional and Fully Connected Layers in Neural Networks

Digital pathology has advanced substantially over the last decade however tumor localization continues to be a challenging problem due to highly complex patterns and textures in the underlying tissue bed. The use of convolutional neural networks (CNNs) to analyze such complex images has been well adopted in digital pathology. However in recent years, the architecture of CNNs have altered with the introduction of inception modules which have shown great promise for classification tasks. In this paper, we propose a modified "transition" module which learns global average pooling layers from filters of varying sizes to encourage class-specific filters at multiple spatial resolutions. We demonstrate the performance of the transition module in AlexNet and ZFNet, for classifying breast tumors in two independent datasets of scanned histology sections, of which the transition module was superior.Comment: This work is to appear at the 3rd workshop on Deep Learning in Medical Image Analysis (DLMIA), MICCAI 201

Mitoparans: mitochondriotoxic cell penetrating peptides and novel inducers of apoptosis.

Acknowledgments The authors would like to thank Keith Holding at the University of Wolverhampton for his outstanding technical support. This work was supported in part by Samantha Dickson Brain Tumour Trust.Introduction: The amphipathic helical peptide mastoparan (MP; H-INLKALAALAKKIL-NH2) inserts into biological membranes to modulate the activity of heterotrimeric G proteins and other targets. Moreover, whilst cell free models of apoptosis demonstrate MP to facilitate mitochondrial permeability transition and release of apoptogenic cytochrome c, MP-induced death of intact cells has been attributed to its non-specific membrane destabilising properties (necrotic mechanisms). However, MP and related peptides are known to activate other signalling systems, including p42/p44 MAP kinases and could therefore, also modulate cell fate and specific apoptotic events. The ability of MP to facilitate mitochondrial permeability in cell free systems has lead to proposals that MP could be of utility in tumour therapeutics provided that it conferred features of cellular penetration and mitochondrial localization. We have recently reported that our highly potent amphipathic MP analogue mitoparan (mitP; [Lys5,8Aib10]MP; Aib = -aminoisobutyric acid) specifically promotes apoptosis of human cancer cells, as was confirmed by in situ TUNEL staining and activation of caspase-3. Moreover, we have also demonstrated that mitP penetrates plasma membranes and redistributes to co-localize with mitochondria. Complementary studies, using isolated mitochondria, further demonstrated that mitP, through co-operation with a protein of the permeability transition pore complex voltage-dependent anion channel (VDAC), induced swelling and permeabilization of mitochondria, leading to the release of the apoptogenic factor cytochrome c. An expanding field of peptide and cell penetrating peptide (CPP) research has focussed on the selective targeting of tumours by engineering constructs that incorporate cell-specific or tissue–specific address motifs. Peptidyl address motifs could enhance the selectivity of drug delivery whilst the improved cellular uptake offered by CPP enhances bioavailability. Thus and as a potential therapeutic strategy, we extended our findings to design target-specific mitP analogues. The integrin-specific address motif RGD and a Fas ligand mimetic WEWT were incorporated by N-terminal acylation of mitP to produce novel tandem-linked chimeric peptides

Structure and spacing of cellulose microfibrils in woody cell walls of dicots

The structure of cellulose microfibrils in situ in wood from the dicotyledonous (hardwood) species cherry and birch, and the vascular tissue from sunflower stems, was examined by wide-angle X-ray and neutron scattering (WAXS and WANS) and small-angle neutron scattering (SANS). Deuteration of accessible cellulose chains followed by WANS showed that these chains were packed at similar spacings to crystalline cellulose, consistent with their inclusion in the microfibril dimensions and with a location at the surface of the microfibrils. Using the Scherrer equation and correcting for considerable lateral disorder, the microfibril dimensions of cherry, birch and sunflower microfibrils perpendicular to the [200] crystal plane were estimated as 3.0, 3.4 and 3.3 nm respectively. The lateral dimensions in other directions were more difficult to correct for disorder but appeared to be 3 nm or less. However for cherry and sunflower, the microfibril spacing estimated by SANS was about 4 nm and was insensitive to the presence of moisture. If the microfibril width was 3 nm as estimated by WAXS, the SANS spacing suggests that a non-cellulosic polymer segment might in places separate the aggregated cellulose microfibrils

Scénariser les 4 piliers de la pédagogie

International audienceLa scénarisation des activités pédagogiques constitue un domaine de recherche stimulant à la croisée de l'informatique et des sciences humaines. Nous constatons que les activités pédagogiques traditionnelles relèvent de plusieurs niveaux de préoccupation : l'organisation générale de l'activité, les étapes d'apprentissage proprement dit, l'observation des comportements des apprenants et de leur appropriation des enseignements, et enfin de l'évaluation de l'activité autant sur le plan des connaissances acquises ou confortées, que des méthodes de travail mises en jeu pour cela ou de la façon de collaborer pour y parvenir. Nous formulons l'hypothèse que les activités pédagogiques en ligne peuvent être modélisées de façon modulaire selon ces quatre piliers fondamentaux « organisation, apprentissage, observation et évaluation » et qu'en corollaire il est possible d'exprimer ces différents points de vue avec un seul et même langage de modélisation pédagogique, LDL répondant pour sa part à cette proposition

BI-RADS BERT & Using Section Segmentation to Understand Radiology Reports

Radiology reports are one of the main forms of communication between radiologists and other clinicians and contain important information for patient care. In order to use this information for research and automated patient care programs, it is necessary to convert the raw text into structured data suitable for analysis. State-of-the-art natural language processing (NLP) domain-specific contextual word embeddings have been shown to achieve impressive accuracy for these tasks in medicine, but have yet to be utilized for section structure segmentation. In this work, we pre-trained a contextual embedding BERT model using breast radiology reports and developed a classifier that incorporated the embedding with auxiliary global textual features in order to perform section segmentation. This model achieved a 98% accuracy at segregating free text reports sentence by sentence into sections of information outlined in the Breast Imaging Reporting and Data System (BI-RADS) lexicon, a significant improvement over the Classic BERT model without auxiliary information. We then evaluated whether using section segmentation improved the downstream extraction of clinically relevant information such as modality/procedure, previous cancer, menopausal status, the purpose of the exam, breast density, and breast MRI background parenchymal enhancement. Using the BERT model pre-trained on breast radiology reports combined with section segmentation resulted in an overall accuracy of 95.9% in the field extraction tasks. This is a 17% improvement compared to an overall accuracy of 78.9% for field extraction with models using Classic BERT embeddings and not using section segmentation. Our work shows the strength of using BERT in radiology report analysis and the advantages of section segmentation in identifying key features of patient factors recorded in breast radiology reports