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The Carnegie Foundation for the Advancement of Teaching joins a chorus of calls for transformation of prelicensure nursing education (Benner, Sutphen, Leonard, & Day, 2009). Citing the shift of significant responsibility to nurses for managing complex medical regimens, as well as the increasing complexity of community-based practices, Benner and colleagues concluded that nurses entering the field are not equipped with the essential knowledge and skills for today's practice nor prepared to continue learning for tomorrow's nursing (p. 31). They found: a) weak curricula in natural sciences, technology, social sciences, and humanities, and in developing cultural competency; b) weak classroom instruction and limited integration between classroom and clinical experiences; c) limited strategies in helping students develop habits of inquiry, raising clinical questions, seeking evidence for practices; d) faculty and student perception that students are ill prepared for their first job and dissatisfaction with the teaching preparation of current nursing faculty; and e) multiple pathways to eligibility for the licensure examination, with tremendous variability in prerequisites, curricular requirements, and the quality of offerings.

Species Meet is a breathtaking meditation on the intersection between humankind dog, philosophy science, macro micro cultures.-Cameron Woo, Publisher of Bark magazine In 2006, about 69 million U.S. households had pets, giving homes to around 73.9 million dogs, 90.5 million cats, 16.6 million birds, spending over $38 billion dollars on animals. As never before in history, our pets are truly members of the family. But the notion of companion speciesd-knotted from human beings, animals other organisms, landscapes, technologies-includes much more than companion animals.d In When Species Meet, Donna J. Haraway digs into this larger phenomenon to contemplate the interactions of humans with many kinds of critters, especially with those called domestic. At the heart of the book are her experiences in agility training with her dogs Cayenne Roland, but Haraway's vision here also encompasses wolves, chickens, cats, baboons, sheep, microorganisms, whales wearing video cameras. From designer pets to lab animals to trained therapy dogs, she deftly explores philosophical, cultural, biological aspects of animal-human encounters. In this deeply personal yet intellectually groundbreaking work, Haraway develops the idea of species, those who meet break bread together but not without some indigestion. A great deal is at stake in such meetings,she writes, and outcomes are not guaranteed. There is no assured happy or unhappy ending-socially, ecologically, or scientifically. There is only the chance for getting on together with some grace.d Ultimately, she finds that respect, curiosity, knowledge spring from animal-human associations work powerfully against ideas about human exceptionalism. One of the founders of the posthumanities, Donna J. Haraway is professor in the History of Consciousness Department at the University of California, Santa Cruz. Author of many books widely read essays, including Companion Species Manifesto: Dogs, People, Significant Otherness the now-classic essay The Cyborg Manifesto,she received the J. D. Bernal Prize in 2000, a lifetime achievement award from the Society for Social Studies in Science.

As recently as a few months ago, I was full of anecdotes of my ownexperience with welfare and welfare reform based on years of representinglow-income clients as a legal-services lawyer and clinical teacher. Since I readMaking Ends Meet,' however, I have a new set of anecdotes, referring to thebook several times a week. In discussions with students, I point out whereEdin and Lein's findings agree (or disagree) with the students' ownobservations. I repeatedly ask colleagues if they have read the book. Ireappraise my clients' work situations in terms of Edin and Lein's findings.My colleague Kathleen Sullivan and I decided to assign the book for ourSpring Community Legal Services clinic, in which students provide legalservices to low-income people in New Haven. In short, I have acted as thoughMaking Ends Meet is a very important book. At the risk of courting hyperbole,Making Ends Meet may be the most important resource we have in trying tofigure which road to take in our ongoing journey toward welfare reform.

Abstract This paper extends earlier work by its authors on formal aspects of the processes of contracting a theory to eliminate a proposition and revising a theory to introduce a proposition. In the course of the earlier work, Gärdenfors developed general postulates of a more or less equational nature for such processes, whilst Alchourrón and Makinson studied the particular case of contraction functions that are maximal, in the sense of yielding a maximal subset of the theory (or alternatively, of one of its axiomatic bases), that fails to imply the proposition being eliminated. In the present paper, the authors study a broader class, including contraction functions that may be less than maximal. Specifically, they investigate “partial meet contraction functions”, which are defined to yield the intersection of some nonempty family of maximal subsets of the theory that fail to imply the proposition being eliminated. Basic properties of these functions are established: it is shown in particular that they satisfy the Gärdenfors postulates, and moreover that they are sufficiently general to provide a representation theorem for those postulates. Some special classes of partial meet contraction functions, notably those that are “relational” and “transitively relational”, are studied in detail, and their connections with certain “supplementary postulates” of Gàrdenfors investigated, with a further representation theorem established.

We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10–100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.

Proposes a new form of business management that focuses on long-range planning, strong corporate philosophy, and concensus decision-making to help American corporations meet the challenge of Japan.

We consider 27 population and community terms used frequently by parasitologists when describing the ecology of parasites. We provide suggestions for various terms in an attempt to foster consistent use and to make terms used in parasite ecology easier to interpret for those who study free-living organisms. We suggest strongly that authors, whether they agree or disagree with us, provide complete and unambiguous definitions for all parameters of their studies.

Classification and identification of the materials lying over or beneath the earth's surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS), and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also investigate a special case of multi-modality learning (MML)-cross-modality learning (CML) that exists widely in RS image classification applications. By focusing on “what,” “where,” and “how” to fuse, we show different fusion strategies as well as how to train deep networks and build the network architecture. Specifically, five fusion architectures are introduced and developed, further being unified in our MDL framework. More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs). To validate the effectiveness and superiority of the MDL framework, extensive experiments related to the settings of MML and CML are conducted on two different multimodal RS data sets. Furthermore, the codes and data sets will be available at https://github.com/danfenghong/IEEE_TGRS_MDL-RS, contributing to the RS community.

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Nanocrystals are fundamental to modern science and technology. Mastery over the shape of a nanocrystal enables control of its properties and enhancement of its usefulness for a given application. Our aim is to present a comprehensive review of current research activities that center on the shape-controlled synthesis of metal nanocrystals. We begin with a brief introduction to nucleation and growth within the context of metal nanocrystal synthesis, followed by a discussion of the possible shapes that a metal nanocrystal might take under different conditions. We then focus on a variety of experimental parameters that have been explored to manipulate the nucleation and growth of metal nanocrystals in solution-phase syntheses in an effort to generate specific shapes. We then elaborate on these approaches by selecting examples in which there is already reasonable understanding for the observed shape control or at least the protocols have proven to be reproducible and controllable. Finally, we highlight a number of applications that have been enabled and/or enhanced by the shape-controlled synthesis of metal nanocrystals. We conclude this article with personal perspectives on the directions toward which future research in this field might take.

NF-kappaB (nuclear factor-kappaB) is a collective name for inducible dimeric transcription factors composed of members of the Rel family of DNA-binding proteins that recognize a common sequence motif. NF-kappaB is found in essentially all cell types and is involved in activation of an exceptionally large number of genes in response to infections, inflammation, and other stressful situations requiring rapid reprogramming of gene expression. NF-kappaB is normally sequestered in the cytoplasm of nonstimulated cells and consequently must be translocated into the nucleus to function. The subcellular location of NF-kappaB is controlled by a family of inhibitory proteins, IkappaBs, which bind NF-kappaB and mask its nuclear localization signal, thereby preventing nuclear uptake. Exposure of cells to a variety of extracellular stimuli leads to the rapid phosphorylation, ubiquitination, and ultimately proteolytic degradation of IkappaB, which frees NF-kappaB to translocate to the nucleus where it regulates gene transcription. NF-kappaB activation represents a paradigm for controlling the function of a regulatory protein via ubiquitination-dependent proteolysis, as an integral part of a phosphorylationbased signaling cascade. Recently, considerable progress has been made in understanding the details of the signaling pathways that regulate NF-kappaB activity, particularly those responding to the proinflammatory cytokines tumor necrosis factor-alpha and interleukin-1. The multisubunit IkappaB kinase (IKK) responsible for inducible IkappaB phosphorylation is the point of convergence for most NF-kappaB-activating stimuli. IKK contains two catalytic subunits, IKKalpha and IKKbeta, both of which are able to correctly phosphorylate IkappaB. Gene knockout studies have shed light on the very different physiological functions of IKKalpha and IKKbeta. After phosphorylation, the IKK phosphoacceptor sites on IkappaB serve as an essential part of a specific recognition site for E3RS(IkappaB/beta-TrCP), an SCF-type E3 ubiquitin ligase, thereby explaining how IKK controls IkappaB ubiquitination and degradation. A variety of other signaling events, including phosphorylation of NF-kappaB, hyperphosphorylation of IKK, induction of IkappaB synthesis, and the processing of NF-kappaB precursors, provide additional mechanisms that modulate the level and duration of NF-kappaB activity.

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.

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The mixing of polymers and nanoparticles is opening pathways for engineering flexible composites that exhibit advantageous electrical, optical, or mechanical properties. Recent advances reveal routes to exploit both enthalpic and entropic interactions so as to direct the spatial distribution of nanoparticles and thereby control the macroscopic performance of the material. For example, by tailoring the particle coating and size, researchers have created self-healing materials for improved sustainability and self-corralling rods for photovoltaic applications. A challenge for future studies is to create hierarchically structured composites in which each sublayer contributes a distinct function to yield a mechanically integrated, multifunctional material.

Based on fundamental chemistry, biotechnology and materials science have developed over the past three decades into today's powerful disciplines which allow the engineering of advanced technical devices and the industrial production of active substances for pharmaceutical and biomedical applications. This review is focused on current approaches emerging at the intersection of materials research, nanosciences, and molecular biotechnology. This novel and highly interdisciplinary field of chemistry is closely associated with both the physical and chemical properties of organic and inorganic nanoparticles, as well as to the various aspects of molecular cloning, recombinant DNA and protein technology, and immunology. Evolutionary optimized biomolecules such as nucleic acids, proteins, and supramolecular complexes of these components, are utilized in the production of nanostructured and mesoscopic architectures from organic and inorganic materials. The highly developed instruments and techniques of today's materials research are used for basic and applied studies of fundamental biological processes.

The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks.

Accurate reproduction of the mechanism of peptide folding in solution and conformational preferences as a function of amino acid sequence is possible with atomic level dynamics simulations. For example, the simulations correctly predict a left-handed 31-helical fold for the β-heptapeptide 1 (the molecular model is shown in the picture) and a right-handed helical fold for the β-hexapeptide 2, as was confirmed by NMR spectroscopy.

Metal-organic frameworks (MOFs), established as a relatively new class of crystalline porous materials with high surface area, structural diversity, and tailorability, attract extensive interest and exhibit a variety of applications, especially in catalysis. Their permanent porosity enables their inherent superiority in confining guest species, particularly small metal nanoparticles (MNPs), for improved catalytic performance and/or the expansion of reaction scope. This is a rapidly developing interdisciplinary research field. In this review, we provide an overview of significant progress in the development of MNP/MOF composites, including various preparation strategies and characterization methods as well as catalytic applications. Special emphasis is placed on synergistic effects between the two components that result in an enhanced performance in heterogeneous catalysis. Finally, the prospects of MNP/MOF composites in catalysis and remaining issues in this field have been indicated.