Adaptive Graph Construction for Isomap Manifold Learning

Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ1 norm. The ℓ1 enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than the classical approach. Next, the proposed approach is applied to two image data sets and achieved improved performances over standard Isomap

A Comparative Study of Two Prediction Models for Brain Tumor Progression

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named Dropout can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region

Prediction of Brain Tumor Progression Using Multiple Histogram Matched MRI Scans

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients\u27 MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans revealed that histograms of MRI scans such as T1, T2, FLAIR etc taken at different times have slight shifts or different shapes. This is because those MRI scans are qualitative instead of quantitative so MRI scans taken at different times or by different scanners might have slightly different scales or have different homogeneities in the scanning region. In this paper, we proposed a method to overcome this difficulty. The overall goal of this study is to assess brain tumor progression by exploring seven patients\u27 complete MRI records scanned during their visits in the past two years. There are ten MRI series in each visit, including FLAIR, T1-weighted, post-contrast T1-weighted, T2-weighted and five DTI derived MRI volumes: ADC, FA, Max, Min and Middle Eigen Values. After registering all series to the corresponding DTI scan at the first visit, we applied a histogram matching algorithm to non-DTI MRI scans to match their histograms to those of the corresponding MRI scans at the first visit. DTI derived series are quantitative and do not require the histogram matching procedure. A machine learning algorithm was then trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit B to visit C. An average of 72% pixel-wise accuracy was achieved for tumor progression prediction from visit B to visit C

Fermi-Dirac statistics and the number theory

We relate the Fermi-Dirac statistics of an ideal Fermi gas in a harmonic trap to partitions of given integers into distinct parts, studied in number theory. Using methods of quantum statistical physics we derive analytic expressions for cumulants of the probability distribution of the number of different partitions.Comment: 7pages, 2 figures, epl.cls, revised versio

Interferonβ-1b Induces the Expression of RGS1 a Negative Regulator of G-Protein Signaling

We present evidence of a link between interferonβ-1b (IFN-β) and G-protein signaling by demonstrating that IFN-β can induce the expression of the negative regulator of G-protein signaling 1 (RGS1). RGS1 reduces G-protein activation and immune cell migration by interacting with heterotrimeric G-proteins and enhancing their intrinsic GTPase activity. In this study, IFN-β treatment resulted in the induction of RGS1 in peripheral blood mononuclear cells (PBMCs), monocytes, T cells, and B cells. Induction of RGS1 by IFN-β was concentration dependent and observed at both the RNA and protein level. Other members of the RGS family were not induced by IFN-β, and induction of RGS1 required the activation of the IFN receptor. In addition, RGS1 induction was observed in PBMCs obtained from IFN-β-treated multiple sclerosis patients suggesting a possible, as yet unexplored, involvement of G-protein regulation in disease treatment. The upregulation of RGS1 by IFN-β has not been previously reported

Recommender Systems with Generative Retrieval

Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful tuple of codewords to serve as a Semantic ID for each item. Given Semantic IDs for items in a user session, a Transformer-based sequence-to-sequence model is trained to predict the Semantic ID of the next item that the user will interact with. To the best of our knowledge, this is the first Semantic ID-based generative model for recommendation tasks. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets. In addition, we show that incorporating Semantic IDs into the sequence-to-sequence model enhances its ability to generalize, as evidenced by the improved retrieval performance observed for items with no prior interaction history.Comment: Preprint versio

Building Block Approach to Systems

Author Institution: Mission Support and Test Services, LLCSlides presented at the 2018 Photonic Doppler Velocimetry (PDV) Users Workshop, Drury Plaza Hotel, Santa Fe, New Mexico, May 16-18, 2018

Stereoselective Metal-Free Reduction of Chiral Imines in Batch and Flow Mode: A Convenient Strategy for the Synthesis of Chiral Active Pharmaceutical Ingredients

he convenient, metal-free reduction of imines that contain an inexpensive and removable chiral auxiliary allowed for the synthesis of the immediate precursors of chiral active pharmaceutical ingredients (APIs). This protocol was carried out under batch and flow conditions to give the correspoding prod- ucts in high yields with almost complete stereocontrol. In the presence of trichlorosilane, an inexpensive and nontoxic reduc- ing agent, and an achiral Lewis base such as N,N-dimethyl- Introduction The pharmaceutical industry is gradually progressing towards enantiopure formulations. Most newly introduced drugs are chiral, and it is expected that approximately 95 % of pharma- ceutical drugs will be chiral by 2020. [1] In this context, chiral amines are considered a class of paramount importance, be- cause they are found in a plethora of compounds such as those of pharmaceutical interest as well as those developed for agro- chemicals, fragrances, and fine chemicals. [2] The reduction of the C=N group is one of the most widely used approaches to synthesize chiral amines, and over the last ten years, successful catalytic enantioselective methods based on both metal-pro- moted [3] and organocatalyzed [4] strategies have been devel- oped. When an industrial synthesis of a chiral pharmaceutical prod- uct must be planned, however, issues such as the chemical effi- ciency and robustness of the procedure, its general applicabil- ity, and economic considerations become crucially important. For these reasons, the applications of many chiral catalytic sys- tems are often not feasible, and the use of inexpensive and readily available chiral auxiliaries becomes an attractive and economic alternative. This also holds true for the synthesis of [a] Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy E-mail: [email protected] http://users2.unimi.it/Benagliagroup [b] Istituto di Scienze e Tecnologie Molecolari ISTM-CNR, Via Golgi 19, 20133 Milano, Italy [c] Department of Chemistry and Chemistry Center of Évora, University of Évora, Rua Romão Ramalho, 59, 7000 Évora, Portugal Supporting information and ORCID(s) from the author(s) for this article are available on the WWW under http://dx.doi.org/10.1002/ejoc.201601268. Eur. J. Org. Chem. 2017, 39–44 © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim39 formamide, the formal syntheses of Rivastgmine, calcimimetic NPS R-568, and a Rho kinases inhibitor were successfully accom- plished. For the first time, both the diastereoselective imine re- duction and the auxiliary removal were efficiently performed in a micro- or mesoreactor under continuous-flow conditions, which paved the way towards the development of a practical process for the syntheses of industrially relevant, biologically active, enantiopure N-alkylamine

Two structurally distinct domains of the nucleoporin Nup170 cooperate to tether a subset of nucleoporins to nuclear pores

How individual nucleoporins (Nups) perform their role in nuclear pore structure and function is largely unknown. In this study, we examined the structure of purified Nup170 to obtain clues about its function. We show that Nup170 adopts a crescent moon shape with two structurally distinct and separable domains, a β-propeller N terminus and an α-solenoid C terminus. To address the individual roles of each domain, we expressed these domains separately in yeast. Notably, overexpression of the Nup170 C domain was toxic in nup170Δ cells and caused accumulation of several Nups in cytoplasmic foci. Further experiments indicated that the C-terminal domain anchors Nup170 to nuclear pores, whereas the N-terminal domain functions to recruit or retain a subset of Nups, including Nup159, Nup188, and Pom34, at nuclear pores. We conclude that Nup170 performs its role as a structural adapter between cytoplasmically oriented Nups and the nuclear pore membrane