Superpixel Convolutional Networks using Bilateral Inceptions

In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception module between the last CNN (1x1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201

2-Propynyl 2-hydroxy­benzoate

The title compound, C10H8O3, has been synthesized as part of our investigations into the generation of new anti­bacterial agents and serves as a building block for the synthesis of compound libraries. The compound crystallizes with two independent mol­ecules in the asymmetric unit. The transoid propynyl ester groups are coplanar with the 2-hydroxy­benzoate group with maximum deviations of −0.3507 (3) and 0.1591 (3) Å for the terminal carbons, with intra­molecular O—H⋯O hydrogen bonding providing rigidity to the structure and ensuring that the reactivity of the alkyne is not compromised by steric factors. The propynyl group forms inter­molecular C—H⋯O inter­actions with the phenolic O atom. Supra­molecular chains along the b axis are found for both mol­ecules with links by weak O—H⋯O inter­molecular inter­actions in the first independent mol­ecule and C—H⋯O inter­actions in the second

Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.Comment: To appear in ECCV 201

α-Synuclein Aggregation Inhibitory Prunolides and a Dibrominated β-Carboline Sulfamate from the Ascidian Synoicum prunum

Seven new polyaromatic bis-spiroketal-containing butenolides, the prunolides D–I (4–9) and cis-prunolide C (10), a new dibrominated β-carboline sulfamate named pityriacitrin C (11), alongside the known prunolides A–C (1–3) were isolated from the Australian colonial ascidian Synoicum prunum. The prunolides D–G (4–7) represent the first asymmetrically brominated prunolides, while cis-prunolide C (10) is the first reported with a cis-configuration about the prunolide’s bis-spiroketal core. The prunolides displayed binding activities with the Parkinson’s disease-implicated amyloid protein α-synuclein in a mass spectrometry binding assay, while the prunolides (1–5 and 10) were found to significantly inhibit the aggregation (>89.0%) of α-synuclein in a ThT amyloid dye assay. The prunolides A–C (1–3) were also tested for inhibition of pSyn aggregate formation in a primary embryonic mouse midbrain dopamine neuron model with prunolide B (2) displaying statistically significant inhibitory activity at 0.5 μM. The antiplasmodial and antibacterial activities of the isolates were also examined with prunolide C (3) displaying only weak activity against the 3D7 parasite strain of Plasmodium falciparum. Our findings reported herein suggest that the prunolides could provide a novel scaffold for the exploration of future therapeutics aimed at inhibiting amyloid protein aggregation and the treatment of numerous neurodegenerative diseases.Peer reviewe

The European Hematology Association Roadmap for European Hematology Research: a consensus document

The European Hematology Association (EHA) Roadmap for European Hematology Research highlights major achievements in diagnosis and treatment of blood disorders and identifies the greatest unmet clinical and scientific needs in those areas to enable better funded, more focused European hematology research. Initiated by the EHA, around 300 experts contributed to the consensus document, which will help European policy makers, research funders, research organizations, researchers, and patient groups make better informed decisions on hematology research. It also aims to raise public awareness of the burden of blood disorders on European society, which purely in economic terms is estimated at €23 billion per year, a level of cost that is not matched in current European hematology research funding. In recent decades, hematology research has improved our fundamental understanding of the biology of blood disorders, and has improved diagnostics and treatments, sometimes in revolutionary ways. This progress highlights the potential of focused basic research programs such as this EHA Roadmap. The EHA Roadmap identifies nine ‘sections’ in hematology: normal hematopoiesis, malignant lymphoid and myeloid diseases, anemias and related diseases, platelet disorders, blood coagulation and hemostatic disorders, transfusion medicine, infections in hematology, and hematopoietic stem cell transplantation. These sections span 60 smaller groups of diseases or disorders. The EHA Roadmap identifies priorities and needs across the field of hematology, including those to develop targeted therapies based on genomic profiling and chemical biology, to eradicate minimal residual malignant disease, and to develop cellular immunotherapies, combination treatments, gene therapies, hematopoietic stem cell treatments, and treatments that are better tolerated by elderly patients

The European Hematology Association Roadmap for European Hematology Research. A Consensus Document

Abstract The European Hematology Association (EHA) Roadmap for European Hematology Research highlights major achievements in diagnosis and treatment of blood disorders and identifies the greatest unmet clinical and scientific needs in those areas to enable better funded, more focused European hematology research. Initiated by the EHA, around 300 experts contributed to the consensus document, which will help European policy makers, research funders, research organizations, researchers, and patient groups make better informed decisions on hematology research. It also aims to raise public awareness of the burden of blood disorders on European society, which purely in economic terms is estimated at Euro 23 billion per year, a level of cost that is not matched in current European hematology research funding. In recent decades, hematology research has improved our fundamental understanding of the biology of blood disorders, and has improved diagnostics and treatments, sometimes in revolutionary ways. This progress highlights the potential of focused basic research programs such as this EHA Roadmap. The EHA Roadmap identifies nine sections in hematology: normal hematopoiesis, malignant lymphoid and myeloid diseases, anemias and related diseases, platelet disorders, blood coagulation and hemostatic disorders, transfusion medicine, infections in hematology, and hematopoietic stem cell transplantation. These sections span 60 smaller groups of diseases or disorders. The EHA Roadmap identifies priorities and needs across the field of hematology, including those to develop targeted therapies based on genomic profiling and chemical biology, to eradicate minimal residual malignant disease, and to develop cellular immunotherapies, combination treatments, gene therapies, hematopoietic stem cell treatments, and treatments that are better tolerated by elderly patients. Received December 15, 2015. Accepted January 27, 2016. Copyright © 2016, Ferrata Storti Foundatio

Probabilistic Progress Bars

Predicting the time at which the integral over a stochastic process reaches a target level is a value of interest in many applications. Often, such computations have to be made at low cost, in real time. As an intuitive example that captures many features of this problem class, we choose progress bars, a ubiquitous element of computer user interfaces. These predictors are usually based on simple point estimators, with no error modelling. This leads to fluctuating behaviour confusing to the user. It also does not provide a distribution prediction (risk values), which are crucial for many other application areas. We construct and empirically evaluate a fast, constant cost algorithm using a Gauss-Markov process model which provides more information to the user