WT1 and its transcriptional cofactor BASP1 redirect the differentiation pathway of an established blood cell line

The Wilms' tumour suppressor WT1 (Wilms' tumour 1) is a transcriptional regulator that plays a central role in organogenesis, and is mutated or aberrantly expressed in several childhood and adult malignancies. We previously identified BASP1 (brain acid-soluble protein 1) as a WT1 cofactor that suppresses the transcriptional activation function of WT1. In the present study we have analysed the dynamic between WT1 and BASP1 in the regulation of gene expression in myelogenous leukaemia K562 cells. Our findings reveal that BASP1 is a significant regulator of WT1 that is recruited to WT1-binding sites and suppresses WT1-mediated transcriptional activation at several WT1 target genes. We find that WT1 and BASP1 can divert the differentiation programme of K562 cells to a non-blood cell type following induction by the phorbol ester PMA. WT1 and BASP1 co-operate to induce the differentiation of K562 cells to a neuronal-like morphology that exhibits extensive arborization, and the expression of several genes involved in neurite outgrowth and synapse formation. Functional analysis revealed the relevance of the transcriptional reprogramming and morphological changes, in that the cells elicited a response to the neurotransmitter ATP. Taken together, the results of the present study reveal that WT1 and BASP1 can divert the lineage potential of an established blood cell line towards a cell with neuronal characteristics

WT1 and its transcriptional cofactor BASP1 redirect the differentiation pathway of an established blood cell line

The Wilms' tumour suppressor WT1 (Wilms' tumour 1) is a transcriptional regulator that plays a central role in organogenesis, and is mutated or aberrantly expressed in several childhood and adult malignancies. We previously identified BASP1 (brain acid-soluble protein 1) as a WT1 cofactor that suppresses the transcriptional activation function of WT1. In the present study we have analysed the dynamic between WT1 and BASP1 in the regulation of gene expression in myelogenous leukaemia K562 cells. Our findings reveal that BASP1 is a significant regulator of WT1 that is recruited to WT1-binding sites and suppresses WT1-mediated transcriptional activation at several WT1 target genes. We find that WT1 and BASP1 can divert the differentiation programme of K562 cells to a non-blood cell type following induction by the phorbol ester PMA. WT1 and BASP1 co-operate to induce the differentiation of K562 cells to a neuronal-like morphology that exhibits extensive arborization, and the expression of several genes involved in neurite outgrowth and synapse formation. Functional analysis revealed the relevance of the transcriptional reprogramming and morphological changes, in that the cells elicited a response to the neurotransmitter ATP. Taken together, the results of the present study reveal that WT1 and BASP1 can divert the lineage potential of an established blood cell line towards a cell with neuronal characteristics

Subitizing with Variational Autoencoders

Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items. In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. In this work, we focus on more complex natural images using unsupervised hierarchical neural networks. Specifically, we show that variational autoencoders are able to spontaneously perform subitizing after training without supervision on a large amount images from the Salient Object Subitizing dataset. While our method is unable to outperform supervised convolutional networks for subitizing, we observe that the networks learn to encode numerosity as basic visual property. Moreover, we find that the learned representations are likely invariant to object area; an observation in alignment with studies on biological neural networks in cognitive neuroscience

Robustness Verification of Support Vector Machines

We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical abstraction of real values and, analogously to the case of neural networks, needs neither abstract least upper bounds nor widening operators on this abstraction. The standard interval domain provides a simple instantiation of our abstraction technique, which is enhanced with the domain of reduced affine forms, which is an efficient abstraction of the zonotope abstract domain. This robustness verification technique has been fully implemented and experimentally evaluated on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels, which have been trained on the popular MNIST dataset of images and on the recent and more challenging Fashion-MNIST dataset. The experimental results of our prototype SVM robustness verifier appear to be encouraging: this automated verification is fast, scalable and shows significantly high percentages of provable robustness on the test set of MNIST, in particular compared to the analogous provable robustness of neural networks

ACL reconstruction with unicondylar replacement in knee with functional instability and osteoarthritis

Severe symptomatic osteoarthritis in young and active patients with pre-existing deficiency of the anterior cruciate ligament and severe functionally instability is a difficult subgroup to manage. There is considerable debate regarding management of young patients with isolated unicompartment osteoarthritis and concomitant ACL deficiency. A retrospective analysis of was done in 9 patients with symptomatic osteoarthritis with ACL deficiencies and functional instability that were treated with unicompartment knee arthroplasty and ACL reconstruction between April 2002 and June 2005. The average arc of flexion was 119° (range 85° to 135°) preoperatively and 125° (range 105° to 140°). There were no signs of instability during the follow up of patients. No patients in this group were reoperated. In this small series we have shown that instability can be corrected and pain relieved by this combined procedure

On the equivalence of pairing correlations and intrinsic vortical currents in rotating nuclei

The present paper establishes a link between pairing correlations in rotating nuclei and collective vortical modes in the intrinsic frame. We show that the latter can be embodied by a simple S-type coupling a la Chandrasekhar between rotational and intrinsic vortical collective modes. This results from a comparison between the solutions of microscopic calculations within the HFB and the HF Routhian formalisms. The HF Routhian solutions are constrained to have the same Kelvin circulation expectation value as the HFB ones. It is shown in several mass regions, pairing regimes, and for various spin values that this procedure yields moments of inertia, angular velocities, and current distributions which are very similar within both formalisms. We finally present perspectives for further studies.Comment: 8 pages, 4 figures, submitted to Phys. Rev.

Sex chromosome positions in human interphase nuclei as studied by in situ hybridization with chromosome specific DNA probes

Two cloned repetitive DNA probes, pXBR and CY1, which bind preferentially to specific regions of the human X and Y chromosome, respectively, were used to study the distribution of the sex chromosomes in human lymphocyte nuclei by in situ hybridization experiments. Our data indicate a large variability of the distances between the sex chromosomes in male and female interphase nuclei. However, the mean distance observed between the X and Y chromosome was significantly smaller than the mean distance observed between the two X-chromosomes. The distribution of distances determined experimentally is compared with three model distributions of distances, and the question of a non-random distribution of sex chromosomes is discussed. Mathematical details of these model distributions are provided in an Appendix to this paper. In the case of a human translocation chromosome (XqterXp22.2::Yq11Y qter) contained in the Chinese hamster x human hybrid cell line 445 x 393, the binding sites of pXBR and CY1 were found close to each other in most interphase nuclei. These data demonstrate the potential use of chromosome-specific repetitive DNA probes to study the problem of interphase chromosome topography

Structured illumination microscopy combined with machine learning for the high throughput analysis of virus structure

Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line

Inferring the dynamics of oscillatory systems using recurrent neural networks

We investigate the predictive power of recurrent neural networks for oscillatory systems not only on the attractor, but in its vicinity as well. For this we consider systems perturbed by an external force. This allows us to not merely predict the time evolution of the system, but also study its dynamical properties, such as bifurcations, dynamical response curves, characteristic exponents etc. It is shown that they can be effectively estimated even in some regions of the state space where no input data were given. We consider several different oscillatory examples, including self-sustained, excitatory, time-delay and chaotic systems. Furthermore, with a statistical analysis we assess the amount of training data required for effective inference for two common recurrent neural network cells, the long short-term memory and the gated recurrent unit.Comment: 9 pages, 7 figure

Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers

P. 307-319Intensive use of heating, ventilation and air conditioning (HVAC) systems in buildings entails an analysis and monitoring of their e ciency. Cooling systems are key facilities in large buildings, and par- ticularly critical in hospitals, where chilled water production is needed as an auxiliary resource for a large number of devices. A chiller plant is often composed of several HVAC units running at the same time, be- ing impossible to assess the individual cooling production and e ciency, since a sensor is seldom installed due to the high cost. We propose a virtual sensor that provides an estimation of the cooling production, based on a deep learning architecture that features a 2D CNN (Convolu- tional Neural Network) to capture relevant features on two-way matrix arrangements of chiller data involving thermodynamic variables and the refrigeration circuits of the chiller unit. Our approach has been tested on an air-cooled chiller in the chiller plant at a hospital, and compared to other state-of-the-art methods using 10-fold cross-validation. Our re- sults report the lowest errors among the tested methods and include a comparison of the true and estimated cooling production and e ciency for a period of several daysS