Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks

In this paper we propose and investigate a novel nonlinear unit, called $L_p$ unit, for deep neural networks. The proposed $L_p$ unit receives signals from several projections of a subset of units in the layer below and computes a normalized $L_p$ norm. We notice two interesting interpretations of the $L_p$ unit. First, the proposed unit can be understood as a generalization of a number of conventional pooling operators such as average, root-mean-square and max pooling widely used in, for instance, convolutional neural networks (CNN), HMAX models and neocognitrons. Furthermore, the $L_p$ unit is, to a certain degree, similar to the recently proposed maxout unit (Goodfellow et al., 2013) which achieved the state-of-the-art object recognition results on a number of benchmark datasets. Secondly, we provide a geometrical interpretation of the activation function based on which we argue that the $L_p$ unit is more efficient at representing complex, nonlinear separating boundaries. Each $L_p$ unit defines a superelliptic boundary, with its exact shape defined by the order $p$. We claim that this makes it possible to model arbitrarily shaped, curved boundaries more efficiently by combining a few $L_p$ units of different orders. This insight justifies the need for learning different orders for each unit in the model. We empirically evaluate the proposed $L_p$ units on a number of datasets and show that multilayer perceptrons (MLP) consisting of the $L_p$ units achieve the state-of-the-art results on a number of benchmark datasets. Furthermore, we evaluate the proposed $L_p$ unit on the recently proposed deep recurrent neural networks (RNN).Comment: ECML/PKDD 201

Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to greatly speed up learning.Comment: 14 pages, 2 figures, 4 listing

Role of homeostasis in learning sparse representations

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components

Vis-NIR luminescent lanthanide-doped core-shell nanoparticles for imaging and photodynamic therapy

The preparation of smart Ln:ZrO2@SiO2 nanoplatforms with grafted photosensitizer (Rose Bengal) which couple optical imaging with photo-dynamic therapy (PDT) is presented. A careful control of the lanthanide dopant loading is considered to enhance the photoemission properties of the lanthanide ions (Er, Pr, Yb) inside the ZrO2 crystal structure. The nanosystem with the lowest lanthanide loading maintains the size, phase and morphology of pristine ZrO2 nanoparticles and exhibit the best performances in term of the overall luminescence properties. Upon functionalization with a silica shell to covalently bound Rose Bengal, a theranostic platform is prepared which is very efficient in singlet oxygen generation, as demonstrated by EPR and UV\u2013vis spectroscopy studies. Preliminary cell viability tests show that while both pristine and Ln doped ZrO2 nanoparticles do not exert cytotoxicity, neither upon illumination nor in dark condition, Rose Bengal grafted samples are able to significantly reduce cell viability under light exposure, thus confirming the high potential of these nanoparticles as PDT tools

Alternating block copolymer-based nanoparticles as tools to modulate the loading of multiple chemotherapeutics and imaging probes

Abstract Cancer therapy often relies on the combined action of different molecules to overcome drug resistance and enhance patient outcome. Combined strategies relying on molecules with different pharmacokinetics often fail due to the lack of concomitant tumor accumulation and, thus, to the loss of synergistic effect. Due to their ability to enhance treatment efficiency, improve drug pharmacokinetics, and reduce adverse effects, polymer nanoparticles (PNPs) have been widely investigated as co-delivery vehicles for cancer therapies. However, co-encapsulation of different drugs and probes in PNPs requires a flexible polymer platform and a tailored particle design, in which both the bulk and surface properties of the carriers are carefully controlled. In this work, we propose a core-shell PNP design based on a polyurethane (PUR) core and a phospholipid external surface. The modulation of the hydrophilic/hydrophobic balance of the PUR core enhanced the encapsulation of two chemotherapeutics with dramatically different water solubility (Doxorubicin hydrochloride, DOXO and Docetaxel, DCTXL) and of Iron Oxide Nanoparticles for MRI imaging. The outer shell remained unchanged among the platforms, resulting in un-modified cellular uptake and in vivo biodistribution. We demonstrate that the choice of PUR core allowed a high entrapment efficiency of all drugs, superior or comparable to previously reported results, and that higher core hydrophilicity enhances the loading efficiency of the hydrophilic DOXO and the MRI contrast effect. Moreover, we show that changing the PUR core did not alter the surface properties of the carriers, since all particles showed a similar behavior in terms of cell internalization and in vivo biodistribution. We also show that PUR PNPs have high passive tumor accumulation and that they can efficient co-deliver the two drugs to the tumor, reaching an 11-fold higher DOXO/DCTXL ratio in tumor as compared to free drugs. Statement of Significance Exploiting the synergistic action of multiple chemotherapeutics is a promising strategy to improve the outcome of cancer patients, as different agents can simultaneously engage different features of tumor cells and/or their microenvironment. Unfortunately, the choice is limited to drugs with similar pharmacokinetics that can concomitantly accumulate in tumors. To expand the spectrum of agents that can be delivered in combination, we propose a multi-compartmental core-shell nanoparticles approach, in which the core is made of biomaterials with high affinity for drugs of different physical properties. We successfully co-encapsulated Doxorubicin Hydrochloride, Docetaxel, and contrast agents and achieved a significantly higher concomitant accumulation in tumor versus free drugs, demonstrating that nanoparticles can improve synergistic cancer chemotherapy

Mitochondrial Alterations Induced by the p13II Protein of Human T-cell Leukemia Virus Type 1 CRITICAL ROLE OF ARGININE RESIDUES

Abstract Human T-cell leukemia virus type 1 encodes a number of "accessory" proteins of unclear function; one of these proteins, p13II, is targeted to mitochondria and disrupts mitochondrial morphology. The present study was undertaken to unravel the function of p13II through (i) determination of its submitochondrial localization and sequences required to alter mitochondrial morphology and (ii) an assessment of the biophysical and biological properties of synthetic peptides spanning residues 9–41 (p139–41), which include the amphipathic mitochondrial-targeting sequence of the protein. p139–41 folded into an α helix in micellar environments. Fractionation and immunogold labeling indicated that full-length p13II accumulates in the inner mitochondrial membrane. p139–41 induced energy-dependent swelling of isolated mitochondria by increasing inner membrane permeability to small cations (Na+, K+) and released Ca2+ from Ca2+-preloaded mitochondria. These effects as well as the ability of full-length p13II to alter mitochondrial morphology in cells required the presence of four arginines, forming the charged face of the targeting signal. The mitochondrial effects of p139–41 were insensitive to cyclosporin A, suggesting that full-length p13II might alter mitochondrial permeability through a permeability transition pore-independent mechanism, thus distinguishing it from the mitochondrial proteins Vpr and X of human immunodeficiency virus type 1 and hepatitis B virus, respectively

Mesenchymal-epithelial signalling in tumour microenvironment: role of high-mobility group Box 1.

Glucose deprivation, hypoxia and acidosis are characteristic features of the central core of most solid tumours. Myofibroblasts are stromal cells present in many such solid tumours, including those of the colon, and are known to be involved in all stages of tumour progression. HMGB1 is a nuclear protein with an important role in nucleosome stabilisation and gene transcription; it is also released from immune cells and is involved in the inflammatory process. We report that the microenvironmental condition of glucose deprivation is responsible for the active release of HMGB1 from various types of cancer cell lines (HT-29, MCF-7 and A549) under normoxic conditions. Recombinant HMGB1 (10 ng/ml) triggered proliferation in myofibroblast cells via activation of PI3K and MEK1/2. Conditioned medium collected from glucose-deprived HT-29 colon cancer cells stimulated the migration and invasion of colonic myofibroblasts, and these processes were significantly inhibited by immunoneutralising antibodies to HMGB1, RAGE and TLR4, together with specific inhibitors of PI3K and MEK1/2. Our data suggest that HMGB1 released from cancer cells under glucose deprivation is involved in stimulating colonic myofibroblast migration and invasion and that this occurs through the activation of RAGE and TLR4, resulting in the activation of the MAPK and PI3K signalling pathways. Thus, HMGB1 might be released by cancer cells in areas of low glucose in solid tumours with the resulting activation of myofibroblasts and is a potential therapeutic target to inhibit solid tumour growth

Adjunctive Brivaracetam in Focal Epilepsy: Real-World Evidence from the BRIVAracetam add-on First Italian netwoRk STudy (BRIVAFIRST)

Background: In randomized controlled trials, add-on brivaracetam (BRV) reduced seizure frequency in patients with drug-resistant focal epilepsy. Studies performed in a naturalistic setting are a useful complement to characterize the drug profile. Objective: This multicentre study assessed the effectiveness and tolerability of adjunctive BRV in a large population of patients with focal epilepsy in the context of real-world clinical practice. Methods: The BRIVAFIRST (BRIVAracetam add-on First Italian netwoRk STudy) was a retrospective, multicentre study including adult patients prescribed adjunctive BRV. Patients with focal epilepsy and 12-month follow-up were considered. Main outcomes included the rates of seizure\u2010freedom, seizure response ( 65 50% reduction in baseline seizure frequency), and treatment discontinuation. The incidence of adverse events (AEs) was also considered. Analyses by levetiracetam (LEV) status and concomitant use of strong enzyme-inducing antiseizure medications (EiASMs) and sodium channel blockers (SCBs) were performed. Results: A total of 1029 patients with a median age of 45 years (33\u201356) was included. At 12 months, 169 (16.4%) patients were seizure-free and 383 (37.2%) were seizure responders. The rate of seizure freedom was 22.3% in LEV-naive patients, 7.1% in patients with prior LEV use and discontinuation due to insufficient efficacy, and 31.2% in patients with prior LEV use and discontinuation due to AEs (p < 0.001); the corresponding values for 65 50% seizure frequency reduction were 47.9%, 29.7%, and 42.8% (p < 0.001). There were no statistically significant differences in seizure freedom and seizure response rates by use of strong EiASMs. The rates of seizure freedom (20.0% vs. 16.6%; p = 0.341) and seizure response (39.7% vs. 26.9%; p = 0.006) were higher in patients receiving SCBs than those not receiving SCBs; 265 (25.8%) patients discontinued BRV. AEs were reported by 30.1% of patients, and were less common in patients treated with BRV and concomitant SCBs than those not treated with SCBs (28.9% vs. 39.8%; p = 0.017). Conclusion: The BRIVAFIRST provided real-world evidence on the effectiveness of BRV in patients with focal epilepsy irrespective of LEV history and concomitant ASMs, and suggested favourable therapeutic combinations

Random subwindows and extremely randomized trees for image classification in cell biology

Background: With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. It stresses the need for computer vision methods that automate image classification tasks. Results: We illustrate the potential of our image classification method in cell biology by evaluating it on four datasets of images related to protein distributions or subcellular localizations, and red-blood cell shapes. Accuracy results are quite good without any specific pre-processing neither domain knowledge incorporation. The method is implemented in Java and available upon request for evaluation and research purpose. Conclusion: Our method is directly applicable to any image classification problems. We foresee the use of this automatic approach as a baseline method and first try on various biological image classification problems