The University of Texas at Dallas

Abstract Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter‐correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross‐validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis (CA) in order to handle qualitative variables and as multiple factor analysis (MFA) in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen‐decomposition of positive semi‐definite matrices and upon the singular value decomposition (SVD) of rectangular matrices. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical and Graphical Methods of Data Analysis > Dimension Reduction

Many potential applications have been proposed for carbon nanotubes, including conductive and high-strength composites; energy storage and energy conversion devices; sensors; field emission displays and radiation sources; hydrogen storage media; and nanometer-sized semiconductor devices, probes, and interconnects. Some of these applications are now realized in products. Others are demonstrated in early to advanced devices, and one, hydrogen storage, is clouded by controversy. Nanotube cost, polydispersity in nanotube type, and limitations in processing and assembly methods are important barriers for some applications of single-walled nanotubes.

The pathophysiological process of Alzheimer's disease (AD) is thought to begin many years before the diagnosis of AD dementia. This long "preclinical" phase of AD would provide a critical opportunity for therapeutic intervention; however, we need to further elucidate the link between the pathological cascade of AD and the emergence of clinical symptoms. The National Institute on Aging and the Alzheimer's Association convened an international workgroup to review the biomarker, epidemiological, and neuropsychological evidence, and to develop recommendations to determine the factors which best predict the risk of progression from "normal" cognition to mild cognitive impairment and AD dementia. We propose a conceptual framework and operational research criteria, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies. These recommendations are solely intended for research purposes and do not have any clinical implications at this time. It is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD, and ultimately, aid the field in moving toward earlier intervention at a stage of AD when some disease-modifying therapies may be most efficacious.

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Supercapacitors, also called ultracapacitors or electrochemical capacitors, store electrical charge on high-surface-area conducting materials. Their widespread use is limited by their low energy storage density and relatively high effective series resistance. Using chemical activation of exfoliated graphite oxide, we synthesized a porous carbon with a Brunauer-Emmett-Teller surface area of up to 3100 square meters per gram, a high electrical conductivity, and a low oxygen and hydrogen content. This sp(2)-bonded carbon has a continuous three-dimensional network of highly curved, atom-thick walls that form primarily 0.6- to 5-nanometer-width pores. Two-electrode supercapacitor cells constructed with this carbon yielded high values of gravimetric capacitance and energy density with organic and ionic liquid electrolytes. The processes used to make this carbon are readily scalable to industrial levels.

Worldwide commercial interest in carbon nanotubes (CNTs) is reflected in a production capacity that presently exceeds several thousand tons per year. Currently, bulk CNT powders are incorporated in diverse commercial products ranging from rechargeable batteries, automotive parts, and sporting goods to boat hulls and water filters. Advances in CNT synthesis, purification, and chemical modification are enabling integration of CNTs in thin-film electronics and large-area coatings. Although not yet providing compelling mechanical strength or electrical or thermal conductivities for many applications, CNT yarns and sheets already have promising performance for applications including supercapacitors, actuators, and lightweight electromagnetic shields.

This is a reprinting of a book originally published in 1978. At that time it was the first book on the subject of homogenization, which is the asymptotic analysis of partial differential equations with rapidly oscillating coefficients, and as such it sets the stage for what problems to consider and what methods to use, including probabilistic methods. At the time the book was written the use of asymptotic expansions with multiple scales was new, especially their use as a theoretical tool, combined with energy methods and the construction of test functions for analysis with weak convergence met

Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.

Clarifies the nature of the entrepreneurial orientation (EO) construct and proposes contingency models for investigating the relationship between EO and firm performance. EO is contrasted to entrepreneurship. Entrepreneurship is new entry. Entrepreneurial orientation is the processes, practices, intentions, and decision-making activities leading to new entry. It has five key dimensions: autonomy, innovativeness, risk taking, proactiveness, and competitive aggressiveness. Although all five dimensions are central to understanding the entrepreneurial process, they occur in different combinations, and the factors vary independently in a given context. Contingency theory suggests that congruence among variables (such as environmental and organizational factors) is crucial for optimal performance; hence the relationship between EO and firm performance is context specific. Key contingencies associated with the relationship between EO and performance are identified. Then four alternative contingency models (moderating, mediating, independent and interaction effects) are proposed for the purpose of testing the relationship of EO and performance. The analysis further delineates various organizational characteristics related to each of the four models that may impact firm performance, including the structure chosen, integrating activities, top management team characteristics and the industry. These characteristics may all affect the five dimensions of entrepreneurial orientation and impact performance. (TNM)

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

There are declines with age in speed of processing, working memory, inhibitory function, and long-term memory, as well as decreases in brain structure size and white matter integrity. In the face of these decreases, functional imaging studies have demonstrated, somewhat surprisingly, reliable increases in prefrontal activation. To account for these joint phenomena, we propose the scaffolding theory of aging and cognition (STAC). STAC provides an integrative view of the aging mind, suggesting that pervasive increased frontal activation with age is a marker of an adaptive brain that engages in compensatory scaffolding in response to the challenges posed by declining neural structures and function. Scaffolding is a normal process present across the lifespan that involves use and development of complementary, alternative neural circuits to achieve a particular cognitive goal. Scaffolding is protective of cognitive function in the aging brain, and available evidence suggests that the ability to use this mechanism is strengthened by cognitive engagement, exercise, and low levels of default network engagement.

Abstract The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal ( https://www.encodeproject.org ), including phase II ENCODE 1 and Roadmap Epigenomics 2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis -regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org ) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

Abstract We investigate the impact of market‐supporting institutions on business strategies by analyzing the entry strategies of foreign investors entering emerging economies. We apply and advance the institution‐based view of strategy by integrating it with resource‐based considerations. In particular, we show how resource‐seeking strategies are pursued using different entry modes in different institutional contexts. Alternative modes of entry—greenfield, acquisition, and joint venture (JV)—allow firms to overcome different kinds of market inefficiencies related to both characteristics of the resources and to the institutional context. In a weaker institutional framework, JVs are used to access many resources, but in a stronger institutional framework, JVs become less important while acquisitions can play a more important role in accessing resources that are intangible and organizationally embedded. Combining survey and archival data from four emerging economies, India, Vietnam, South Africa, and Egypt, we provide empirical support for our hypotheses. Copyright © 2008 John Wiley &amp; Sons, Ltd.

ABSTRACT We examine the relationship between disclosure of nonfinancial information and analyst forecast accuracy using firm-level data from 31 countries. We use the issuance of stand-alone corporate social responsibility (CSR) reports to proxy for disclosure of nonfinancial information. We find that the issuance of stand-alone CSR reports is associated with lower analyst forecast error. This relationship is stronger in countries that are more stakeholder-oriented—i.e., in countries where CSR performance is more likely to affect firm financial performance. The relationship is also stronger for firms and countries with more opaque financial disclosure, suggesting that issuance of stand-alone CSR reports plays a role complementary to financial disclosure. These results hold after we control for various factors related to firm financial transparency and other potentially confounding institutional factors. Collectively, our findings have important implications for academics and practitioners in understanding the function of CSR disclosure in financial markets. Data Availability: The data are publicly available from the sources identified in the paper.

AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.

By introducing twist during spinning of multiwalled carbon nanotubes from nanotube forests to make multi-ply, torque-stabilized yarns, we achieve yarn strengths greater than 460 megapascals. These yarns deform hysteretically over large strain ranges, reversibly providing up to 48% energy damping, and are nearly as tough as fibers used for bulletproof vests. Unlike ordinary fibers and yarns, these nanotube yarns are not degraded in strength by overhand knotting. They also retain their strength and flexibility after heating in air at 450 degrees C for an hour or when immersed in liquid nitrogen. High creep resistance and high electrical conductivity are observed and are retained after polymer infiltration, which substantially increases yarn strength.

Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), or large intelligent surfaces (LISs), <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) of communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multiantenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for applications to next-generation networks. <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> Without loss of generality, we use the name of RIS in the remainder of this paper. </fn>

Individual carbon nanotubes are like minute bits of string, and many trillions of these invisible strings must be assembled to make useful macroscopic articles. We demonstrated such assembly at rates above 7 meters per minute by cooperatively rotating carbon nanotubes in vertically oriented nanotube arrays (forests) and made 5-centimeter-wide, meter-long transparent sheets. These self-supporting nanotube sheets are initially formed as a highly anisotropic electronically conducting aerogel that can be densified into strong sheets that are as thin as 50 nanometers. The measured gravimetric strength of orthogonally oriented sheet arrays exceeds that of sheets of high-strength steel. These nanotube sheets have been used in laboratory demonstrations for the microwave bonding of plastics and for making transparent, highly elastomeric electrodes; planar sources of polarized broad-band radiation; conducting appliqués; and flexible organic light-emitting diodes.

We present a laboratory experiment that measures the effects of induced group identity on social preferences. We find that when participants are matched with an ingroup member, they show a 47 percent increase in charity concerns and a 93 percent decrease in envy. Likewise, participants are 19 percent more likely to reward an ingroup match for good behavior, but 13 percent less likely to punish an ingroup match for misbehavior. Furthermore, participants are significantly more likely to choose social-welfare-maximizing actions when matched with an ingroup member. All results are consistent with the hypothesis that participants are more altruistic toward an ingroup match. (JEL C91, D03, Z13)

A flatter route to shorter channels High-performance silicon transistors can have gate lengths as short as 5 nm before source-drain tunneling and loss of electrostatic control lead to unacceptable leakage current when the device is off. Desai et al. explored the use of MoS 2 as a channel material, given that its electronic properties as thin layers should limit such leakage. A transistor with a 1-nm physical gate was constructed with a MoS 2 bilayer channel and a single-walled carbon nanotube gate electrode. Excellent switching characteristics and an on-off state current ratio of ∼10 6 were observed. Science , this issue p. 99