Showing papers by "New York University published in 2013"

Astropy: A community Python package for astronomy

Abstract: We present the first public version (v02) of the open-source and community-developed Python package, Astropy This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as flexible image transport system (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions Significant functionality is under activedevelopment, such as a model fitting framework, VO client and server tools, and aperture and point spread function (PSF) photometry tools The core development team is actively making additions and enhancements to the current code base, and we encourage anyone interested to participate in the development of future Astropy versions

emcee: The MCMC Hammer

Abstract: We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ~N2 for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at http://dan.iel.fm/emcee under the GNU General Public License v2.

Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!

Abstract: The global bandwidth shortage facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks. There is, however, little knowledge about cellular mm-wave propagation in densely populated indoor and outdoor environments. Obtaining this information is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. In this paper, we present the motivation for new mm-wave cellular systems, methodology, and hardware for measurements and offer a variety of measurement results that show 28 and 38 GHz frequencies can be used when employing steerable directional antennas at base stations and mobile devices.

Sleep Drives Metabolite Clearance From the Adult Brain

Abstract: The conservation of sleep across all animal species suggests that sleep serves a vital function. We here report that sleep has a critical function in ensuring metabolic homeostasis. Using real-time assessments of tetramethylammonium diffusion and two-photon imaging in live mice, we show that natural sleep or anesthesia are associated with a 60% increase in the interstitial space, resulting in a striking increase in convective exchange of cerebrospinal fluid with interstitial fluid. In turn, convective fluxes of interstitial fluid increased the rate of β-amyloid clearance during sleep. Thus, the restorative function of sleep may be a consequence of the enhanced removal of potentially neurotoxic waste products that accumulate in the awake central nervous system.

Visualizing and Understanding Convolutional Networks

Abstract: Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. We also perform an ablation study to discover the performance contribution from different model layers. This enables us to find model architectures that outperform Krizhevsky \etal on the ImageNet classification benchmark. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.

Learning Hierarchical Features for Scene Labeling

Abstract: Scene labeling consists of labeling each pixel in an image with the category of the object it belongs to. We propose a method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel. The method alleviates the need for engineered features, and produces a powerful representation that captures texture, shape, and contextual information. We report results using multiple postprocessing methods to produce the final labeling. Among those, we propose a technique to automatically retrieve, from a pool of segmentation components, an optimal set of components that best explain the scene; these components are arbitrary, for example, they can be taken from a segmentation tree or from any family of oversegmentations. The system yields record accuracies on the SIFT Flow dataset (33 classes) and the Barcelona dataset (170 classes) and near-record accuracy on Stanford background dataset (eight classes), while being an order of magnitude faster than competing approaches, producing a 320×240 image labeling in less than a second, including feature extraction.

Regularization of Neural Networks using DropConnect

Abstract: We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recognition benchmarks by aggregating multiple DropConnect-trained models.

The State of US Health, 1990-2010: Burden of Diseases, Injuries, and Risk Factors

Abstract: Importance Understanding the major health problems in the United States and how they are changing over time is critical for informing national health policy. Objectives To measure the burden of diseases, injuries, and leading risk factors in the United States from 1990 to 2010 and to compare these measurements with those of the 34 countries in the Organisation for Economic Co-operation and Development (OECD) countries. Design We used the systematic analysis of descriptive epidemiology of 291 diseases and injuries, 1160 sequelae of these diseases and injuries, and 67 risk factors or clusters of risk factors from 1990 to 2010 for 187 countries developed for the Global Burden of Disease 2010 Study to describe the health status of the United States and to compare US health outcomes with those of 34 OECD countries. Years of life lost due to premature mortality (YLLs) were computed by multiplying the number of deaths at each age by a reference life expectancy at that age. Years lived with disability (YLDs) were calculated by multiplying prevalence (based on systematic reviews) by the disability weight (based on population-based surveys) for each sequela; disability in this study refers to any short- or long-term loss of health. Disability-adjusted life-years (DALYs) were estimated as the sum of YLDs and YLLs. Deaths and DALYs related to risk factors were based on systematic reviews and meta-analyses of exposure data and relative risks for risk-outcome pairs. Healthy life expectancy (HALE) was used to summarize overall population health, accounting for both length of life and levels of ill health experienced at different ages. Results US life expectancy for both sexes combined increased from 75.2 years in 1990 to 78.2 years in 2010; during the same period, HALE increased from 65.8 years to 68.1 years. The diseases and injuries with the largest number of YLLs in 2010 were ischemic heart disease, lung cancer, stroke, chronic obstructive pulmonary disease, and road injury. Age-standardized YLL rates increased for Alzheimer disease, drug use disorders, chronic kidney disease, kidney cancer, and falls. The diseases with the largest number of YLDs in 2010 were low back pain, major depressive disorder, other musculoskeletal disorders, neck pain, and anxiety disorders. As the US population has aged, YLDs have comprised a larger share of DALYs than have YLLs. The leading risk factors related to DALYs were dietary risks, tobacco smoking, high body mass index, high blood pressure, high fasting plasma glucose, physical inactivity, and alcohol use. Among 34 OECD countries between 1990 and 2010, the US rank for the age-standardized death rate changed from 18th to 27th, for the age-standardized YLL rate from 23rd to 28th, for the age-standardized YLD rate from 5th to 6th, for life expectancy at birth from 20th to 27th, and for HALE from 14th to 26th. Conclusions and Relevance From 1990 to 2010, the United States made substantial progress in improving health. Life expectancy at birth and HALE increased, all-cause death rates at all ages decreased, and age-specific rates of years lived with disability remained stable. However, morbidity and chronic disability now account for nearly half of the US health burden, and improvements in population health in the United States have not kept pace with advances in population health in other wealthy nations.

Astropy: A Community Python Package for Astronomy

Abstract: We present the first public version (v0.2) of the open-source and community-developed Python package, Astropy. This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as Flexible Image Transport System (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions. Significant functionality is under active development, such as a model fitting framework, VO client and server tools, and aperture and point spread function (PSF) photometry tools. The core development team is actively making additions and enhancements to the current code base, and we encourage anyone interested to participate in the development of future Astropy versions.

The baryon oscillation spectroscopic survey of sdss-iii

Abstract: The Baryon Oscillation Spectroscopic Survey (BOSS) is designed to measure the scale of baryon acoustic oscillations (BAO) in the clustering of matter over a larger volume than the combined efforts of all previous spectroscopic surveys of large-scale structure. BOSS uses 1.5 million luminous galaxies as faint as i = 19.9 over 10,000 deg2 to measure BAO to redshifts z < 0.7. Observations of neutral hydrogen in the Lyα forest in more than 150,000 quasar spectra (g < 22) will constrain BAO over the redshift range 2.15 < z < 3.5. Early results from BOSS include the first detection of the large-scale three-dimensional clustering of the Lyα forest and a strong detection from the Data Release 9 data set of the BAO in the clustering of massive galaxies at an effective redshift z = 0.57. We project that BOSS will yield measurements of the angular diameter distance dA to an accuracy of 1.0% at redshifts z = 0.3 and z = 0.57 and measurements of H(z) to 1.8% and 1.7% at the same redshifts. Forecasts for Lyα forest constraints predict a measurement of an overall dilation factor that scales the highly degenerate DA (z) and H –1(z) parameters to an accuracy of 1.9% at z ~ 2.5 when the survey is complete. Here, we provide an overview of the selection of spectroscopic targets, planning of observations, and analysis of data and data quality of BOSS.

Macrophages in atherosclerosis: a dynamic balance

Abstract: Atherosclerosis is a chronic inflammatory disease that arises from an imbalance in lipid metabolism and a maladaptive immune response driven by the accumulation of cholesterol-laden macrophages in the artery wall. Through the analysis of the progression and regression of atherosclerosis in animal models, there is a growing understanding that the balance of macrophages in the plaque is dynamic and that both macrophage numbers and the inflammatory phenotype influence plaque fate. In this Review, we summarize recently identified pro- and anti-inflammatory pathways that link lipid and inflammation biology with the retention of macrophages in plaques, as well as factors that have the potential to promote their egress from these sites.

MEG and EEG data analysis with MNE-Python

Abstract: Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.

Living Crystals of Light-Activated Colloidal Surfers

Abstract: Spontaneous formation of colonies of bacteria or flocks of birds are examples of self-organization in active living matter. Here, we demonstrate a form of self-organization from nonequilibrium driving forces in a suspension of synthetic photoactivated colloidal particles. They lead to two-dimensional "living crystals," which form, break, explode, and re-form elsewhere. The dynamic assembly results from a competition between self-propulsion of particles and an attractive interaction induced respectively by osmotic and phoretic effects and activated by light. We measured a transition from normal to giant-number fluctuations. Our experiments are quantitatively described by simple numerical simulations. We show that the existence of the living crystals is intrinsically related to the out-of-equilibrium collisions of the self-propelled particles.

Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis

Abstract: Rheumatoid arthritis (RA) is a prevalent systemic autoimmune disease, caused by a combination of genetic and environmental factors. Animal models suggest a role for intestinal bacteria in supporting the systemic immune response required for joint inflammation. Here we performed 16S sequencing on 114 stool samples from rheumatoid arthritis patients and controls, and shotgun sequencing on a subset of 44 such samples. We identified the presence of Prevotella copri as strongly correlated with disease in new-onset untreated rheumatoid arthritis (NORA) patients. Increases in Prevotella abundance correlated with a reduction in Bacteroides and a loss of reportedly beneficial microbes in NORA subjects. We also identified unique Prevotella genes that correlated with disease. Further, colonization of mice revealed the ability of P. copri to dominate the intestinal microbiota and resulted in an increased sensitivity to chemically induced colitis. This work identifies a potential role for P. copri in the pathogenesis of RA.

Memory, navigation and theta rhythm in the hippocampal-entorhinal system

Abstract: In this review, Gyorgy Buzsaki and Edvard Moser discuss the most recent evidence suggesting that the navigation and memory functions of the hippocampus and entorhinal cortex are supported by the same neuronal algorithms. They propose that the mechanisms fueling the memory and mental travel engines in the hippocampal-entorhinal system evolved from the mechanisms supporting navigation in the physical world.

Evaluating Amazon's Mechanical Turk as a Tool for Experimental Behavioral Research

Abstract: Amazon Mechanical Turk (AMT) is an online crowdsourcing service where anonymous online workers complete web-based tasks for small sums of money. The service has attracted attention from experimental psychologists interested in gathering human subject data more efficiently. However, relative to traditional laboratory studies, many aspects of the testing environment are not under the experimenter's control. In this paper, we attempt to empirically evaluate the fidelity of the AMT system for use in cognitive behavioral experiments. These types of experiment differ from simple surveys in that they require multiple trials, sustained attention from participants, comprehension of complex instructions, and millisecond accuracy for response recording and stimulus presentation. We replicate a diverse body of tasks from experimental psychology including the Stroop, Switching, Flanker, Simon, Posner Cuing, attentional blink, subliminal priming, and category learning tasks using participants recruited using AMT. While most of replications were qualitatively successful and validated the approach of collecting data anonymously online using a web-browser, others revealed disparity between laboratory results and online results. A number of important lessons were encountered in the process of conducting these replications that should be of value to other researchers.

Invariant Scattering Convolution Networks

Abstract: A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. It cascades wavelet transform convolutions with nonlinear modulus and averaging operators. The first network layer outputs SIFT-type descriptors, whereas the next layers provide complementary invariant information that improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State-of-the-art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.

Intriguing properties of neural networks

Abstract: Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.

A compendium of RNA-binding motifs for decoding gene regulation

Abstract: RNA-binding proteins are key regulators of gene expression, yet only a small fraction have been functionally characterized. Here we report a systematic analysis of the RNA motifs recognized by RNA-binding proteins, encompassing 205 distinct genes from 24 diverse eukaryotes. The sequence specificities of RNA-binding proteins display deep evolutionary conservation, and the recognition preferences for a large fraction of metazoan RNA-binding proteins can thus be inferred from their RNA-binding domain sequence. The motifs that we identify in vitro correlate well with in vivo RNA-binding data. Moreover, we can associate them with distinct functional roles in diverse types of post-transcriptional regulation, enabling new insights into the functions of RNA-binding proteins both in normal physiology and in human disease. These data provide an unprecedented overview of RNA-binding proteins and their targets, and constitute an invaluable resource for determining post-transcriptional regulatory mechanisms in eukaryotes.

De novo mutations in epileptic encephalopathies

Abstract: Epileptic encephalopathies are a devastating group of severe childhood epilepsy disorders for which the cause is often unknown. Here we report a screen for de novo mutations in patients with two classical epileptic encephalopathies: infantile spasms (n = 149) and Lennox-Gastaut syndrome (n = 115). We sequenced the exomes of 264 probands, and their parents, and confirmed 329 de novo mutations. A likelihood analysis showed a significant excess of de novo mutations in the ∼4,000 genes that are the most intolerant to functional genetic variation in the human population (P = 2.9 × 10(-3)). Among these are GABRB3, with de novo mutations in four patients, and ALG13, with the same de novo mutation in two patients; both genes show clear statistical evidence of association with epileptic encephalopathy. Given the relevant site-specific mutation rates, the probabilities of these outcomes occurring by chance are P = 4.1 × 10(-10) and P = 7.8 × 10(-12), respectively. Other genes with de novo mutations in this cohort include CACNA1A, CHD2, FLNA, GABRA1, GRIN1, GRIN2B, HNRNPU, IQSEC2, MTOR and NEDD4L. Finally, we show that the de novo mutations observed are enriched in specific gene sets including genes regulated by the fragile X protein (P < 10(-8)), as has been reported previously for autism spectrum disorders.

The importance of mixed selectivity in complex cognitive tasks

Abstract: Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input–output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons. Neurophysiology experiments in behaving animals are often analysed and modelled with a reverse engineering perspective, with the more or less explicit intention to identify highly specialized components with distinct functional roles in the behaviour under study. Physiologists often find the components they are looking for, contributing to the understanding of the functions and the underlying mechanisms of various brain areas, but they are also bewildered by numerous observations that are difficult to interpret. Many cells, especially in higherorder brain structures like the prefrontal cortex (PFC), often have complex and diverse response properties that are not organized anatomically, and that simultaneously reflect different parameters. These neurons are said to have mixed selectivity to multiple aspects of the task. For instance, in rule-based sensory-motor mapping tasks (such as the Wisconsin card sorting test), the response of a PFC cell may be correlated with parameters of the sensory stimuli, task rule, motor response or any combination of these 1,2 . The predominance of these mixed selectivity neurons seems to be a hallmark of PFC and other brain structures involved in cognition. Understanding such neural representations has been a major conceptual challenge in the field. To characterize the statistics and understand the functional role of mixed selectivity, we analysed neural activity recorded in the PFC of monkeys during a sequence memory task 3 . Motivated by recent theoretical advances in understanding how machine learning principles play out in the functioning of neuronal circuits 4–10 , we devised a new analysis of the recorded population activity. This analysis revealed that the observed mixed selectivity can be naturally understood as a signature of the information-encoding strategy of state-of-the-art classifiers like support vector machines 11 . Specifically we found that (1) the populations of recorded neurons encode distributed information that is not contained in classical selectivity to individual task aspects, (2) the recorded neural representations are high-dimensional, and (3) the dimensionality of the recorded neural representations predicts behavioural performance.

Macropinocytosis of protein is an amino acid supply route in Ras-transformed cells

Abstract: Macropinocytosis is a highly conserved endocytic process by which extracellular fluid and its contents are internalized into cells through large, heterogeneous vesicles known as macropinosomes. Oncogenic Ras proteins have been shown to stimulate macropinocytosis but the functional contribution of this uptake mechanism to the transformed phenotype remains unknown. Here we show that Ras-transformed cells use macropinocytosis to transport extracellular protein into the cell. The internalized protein undergoes proteolytic degradation, yielding amino acids including glutamine that can enter central carbon metabolism. Accordingly, the dependence of Ras-transformed cells on free extracellular glutamine for growth can be suppressed by the macropinocytic uptake of protein. Consistent with macropinocytosis representing an important route of nutrient uptake in tumours, its pharmacological inhibition compromises the growth of Ras-transformed pancreatic tumour xenografts. These results identify macropinocytosis as a mechanism by which cancer cells support their unique metabolic needs and point to the possible exploitation of this process in the design of anticancer therapies.

European Society of Hypertension Position Paper on Ambulatory Blood Pressure Monitoring

Abstract: Ambulatory blood pressure monitoring (ABPM) is being used increasingly in both clinical practice and hypertension research. Although there are many guidelines that emphasize the indications for ABPM, there is no comprehensive guideline dealing with all aspects of the technique. It was agreed at a consensus meeting on ABPM in Milan in 2011 that the 34 attendees should prepare a comprehensive position paper on the scientific evidence for ABPM.This position paper considers the historical background, the advantages and limitations of ABPM, the threshold levels for practice, and the cost-effectiveness of the technique. It examines the need for selecting an appropriate device, the accuracy of devices, the additional information and indices that ABPM devices may provide, and the software requirements.At a practical level, the paper details the requirements for using ABPM in clinical practice, editing considerations, the number of measurements required, and the circumstances, such as obesity and arrhythmias, when particular care needs to be taken when using ABPM.The clinical indications for ABPM, among which white-coat phenomena, masked hypertension, and nocturnal hypertension appear to be prominent, are outlined in detail along with special considerations that apply in certain clinical circumstances, such as childhood, the elderly and pregnancy, and in cardiovascular illness, examples being stroke and chronic renal disease, and the place of home measurement of blood pressure in relation to ABPM is appraised.The role of ABPM in research circumstances, such as pharmacological trials and in the prediction of outcome in epidemiological studies is examined and finally the implementation of ABPM in practice is considered in relation to the issue of reimbursement in different countries, the provision of the technique by primary care practices, hospital clinics and pharmacies, and the growing role of registries of ABPM in many countries.

On Ideal Lattices and Learning with Errors over Rings

Abstract: The “learning with errors” (LWE) problem is to distinguish random linear equations, which have been perturbed by a small amount of noise, from truly uniform ones. The problem has been shown to be as hard as worst-case lattice problems, and in recent years it has served as the foundation for a plethora of cryptographic applications. Unfortunately, these applications are rather inefficient due to an inherent quadratic overhead in the use of LWE. A main open question was whether LWE and its applications could be made truly efficient by exploiting extra algebraic structure, as was done for lattice-based hash functions (and related primitives).We resolve this question in the affirmative by introducing an algebraic variant of LWE called ring-LWE, and proving that it too enjoys very strong hardness guarantees. Specifically, we show that the ring-LWE distribution is pseudorandom, assuming that worst-case problems on ideal lattices are hard for polynomial-time quantum algorithms. Applications include the first truly practical lattice-based public-key cryptosystem with an efficient security reduction; moreover, many of the other applications of LWE can be made much more efficient through the use of ring-LWE.

Data science and its relationship to big data and data-driven decision making

Abstract: Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

Macropinocytosis of protein is an amino acid supply route in Ras-transformed cells

Abstract: Macropinocytosis is a highly conserved endocytic process by which extracellular fluid and its contents are internalized into cells through large, heterogeneous vesicles known as macropinosomes. Oncogenic Ras proteins have been shown to stimulate macropinocytosis but the functional contribution of this uptake mechanism to the transformed phenotype remains unknown. Here we show that Ras-transformed cells use macropinocytosis to transport extracellular protein into the cell. The internalized protein undergoes proteolytic degradation, yielding amino acids including glutamine that can enter central carbon metabolism. Accordingly, the dependence of Ras-transformed cells on free extracellular glutamine for growth can be suppressed by the macropinocytic uptake of protein. Consistent with macropinocytosis representing an important route of nutrient uptake in tumours, its pharmacological inhibition compromises the growth of Ras-transformed pancreatic tumour xenografts. These results identify macropinocytosis as a mechanism by which cancer cells support their unique metabolic needs and point to the possible exploitation of this process in the design of anticancer therapies.

The Multi-object, Fiber-fed Spectrographs for the Sloan Digital Sky Survey and the Baryon Oscillation Spectroscopic Survey

Abstract: We present the design and performance of the multi-object fiber spectrographs for the Sloan Digital Sky Survey (SDSS) and their upgrade for the Baryon Oscillation Spectroscopic Survey (BOSS). Originally commissioned in Fall 1999 on the 2.5 m aperture Sloan Telescope at Apache Point Observatory, the spectrographs produced more than 1.5 million spectra for the SDSS and SDSS-II surveys, enabling a wide variety of Galactic and extra-galactic science including the first observation of baryon acoustic oscillations in 2005. The spectrographs were upgraded in 2009 and are currently in use for BOSS, the flagship survey of the third-generation SDSS-III project. BOSS will measure redshifts of 1.35 million massive galaxies to redshift 0.7 and Lyα absorption of 160,000 high redshift quasars over 10,000 deg2 of sky, making percent level measurements of the absolute cosmic distance scale of the universe and placing tight constraints on the equation of state of dark energy. The twin multi-object fiber spectrographs utilize a simple optical layout with reflective collimators, gratings, all-refractive cameras, and state-of-the-art CCD detectors to produce hundreds of spectra simultaneously in two channels over a bandpass covering the near-ultraviolet to the near-infrared, with a resolving power R = λ/FWHM ~ 2000. Building on proven heritage, the spectrographs were upgraded for BOSS with volume-phase holographic gratings and modern CCD detectors, improving the peak throughput by nearly a factor of two, extending the bandpass to cover 360 nm < λ < 1000 nm, and increasing the number of fibers from 640 to 1000 per exposure. In this paper we describe the original SDSS spectrograph design and the upgrades implemented for BOSS, and document the predicted and measured performances.