Showing papers by "University of Texas at Austin published in 2017"

NF-κB signaling in inflammation

Abstract: The transcription factor NF-κB regulates multiple aspects of innate and adaptive immune functions and serves as a pivotal mediator of inflammatory responses. NF-κB induces the expression of various pro-inflammatory genes, including those encoding cytokines and chemokines, and also participates in inflammasome regulation. In addition, NF-κB plays a critical role in regulating the survival, activation and differentiation of innate immune cells and inflammatory T cells. Consequently, deregulated NF-κB activation contributes to the pathogenic processes of various inflammatory diseases. In this review, we will discuss the activation and function of NF-κB in association with inflammatory diseases and highlight the development of therapeutic strategies based on NF-κB inhibition.

Lithium battery chemistries enabled by solid-state electrolytes

Abstract: Solid-state electrolytes are attracting increasing interest for electrochemical energy storage technologies. In this Review, we provide a background overview and discuss the state of the art, ion-transport mechanisms and fundamental properties of solid-state electrolyte materials of interest for energy storage applications. We focus on recent advances in various classes of battery chemistries and systems that are enabled by solid electrolytes, including all-solid-state lithium-ion batteries and emerging solid-electrolyte lithium batteries that feature cathodes with liquid or gaseous active materials (for example, lithium–air, lithium–sulfur and lithium–bromine systems). A low-cost, safe, aqueous electrochemical energy storage concept with a ‘mediator-ion’ solid electrolyte is also discussed. Advanced battery systems based on solid electrolytes would revitalize the rechargeable battery field because of their safety, excellent stability, long cycle lives and low cost. However, great effort will be needed to implement solid-electrolyte batteries as viable energy storage systems. In this context, we discuss the main issues that must be addressed, such as achieving acceptable ionic conductivity, electrochemical stability and mechanical properties of the solid electrolytes, as well as a compatible electrolyte/electrode interface. This Review details recent advances in battery chemistries and systems enabled by solid electrolytes, including all-solid-state lithium-ion, lithium–air, lithium–sulfur and lithium–bromine batteries, as well as an aqueous battery concept with a mediator-ion solid electrolyte.

Gravitational Waves and Gamma-Rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A

Abstract: On 2017 August 17, the gravitational-wave event GW170817 was observed by the Advanced LIGO and Virgo detectors, and the gamma-ray burst (GRB) GRB 170817A was observed independently by the Fermi Gamma-ray Burst Monitor, and the Anti-Coincidence Shield for the Spectrometer for the International Gamma-Ray Astrophysics Laboratory. The probability of the near-simultaneous temporal and spatial observation of GRB 170817A and GW170817 occurring by chance is $5.0\times {10}^{-8}$. We therefore confirm binary neutron star mergers as a progenitor of short GRBs. The association of GW170817 and GRB 170817A provides new insight into fundamental physics and the origin of short GRBs. We use the observed time delay of $(+1.74\pm 0.05)\,{\rm{s}}$ between GRB 170817A and GW170817 to: (i) constrain the difference between the speed of gravity and the speed of light to be between $-3\times {10}^{-15}$ and $+7\times {10}^{-16}$ times the speed of light, (ii) place new bounds on the violation of Lorentz invariance, (iii) present a new test of the equivalence principle by constraining the Shapiro delay between gravitational and electromagnetic radiation. We also use the time delay to constrain the size and bulk Lorentz factor of the region emitting the gamma-rays. GRB 170817A is the closest short GRB with a known distance, but is between 2 and 6 orders of magnitude less energetic than other bursts with measured redshift. A new generation of gamma-ray detectors, and subthreshold searches in existing detectors, will be essential to detect similar short bursts at greater distances. Finally, we predict a joint detection rate for the Fermi Gamma-ray Burst Monitor and the Advanced LIGO and Virgo detectors of 0.1–1.4 per year during the 2018–2019 observing run and 0.3–1.7 per year at design sensitivity.

First-Line Nivolumab in Stage IV or Recurrent Non–Small-Cell Lung Cancer

Abstract: BackgroundNivolumab has been associated with longer overall survival than docetaxel among patients with previously treated non–small-cell lung cancer (NSCLC). In an open-label phase 3 trial, we compared first-line nivolumab with chemotherapy in patients with programmed death ligand 1 (PD-L1)–positive NSCLC. MethodsWe randomly assigned, in a 1:1 ratio, patients with untreated stage IV or recurrent NSCLC and a PD-L1 tumor-expression level of 1% or more to receive nivolumab (administered intravenously at a dose of 3 mg per kilogram of body weight once every 2 weeks) or platinum-based chemotherapy (administered once every 3 weeks for up to six cycles). Patients receiving chemotherapy could cross over to receive nivolumab at the time of disease progression. The primary end point was progression-free survival, as assessed by means of blinded independent central review, among patients with a PD-L1 expression level of 5% or more. ResultsAmong the 423 patients with a PD-L1 expression level of 5% or more, the media...

Maximizing the right stuff: The trade-off between membrane permeability and selectivity

Abstract: BACKGROUND Synthetic membranes are used for desalination, dialysis, sterile filtration, food processing, dehydration of air and other industrial, medical, and environmental applications due to low energy requirements, compact design, and mechanical simplicity. New applications are emerging from the water-energy nexus, shale gas extraction, and environmental needs such as carbon capture. All membranes exhibit a trade-off between permeability—i.e., how fast molecules pass through a membrane material—and selectivity—i.e., to what extent the desired molecules are separated from the rest. However, biological membranes such as aquaporins and ion channels are both highly permeable and highly selective. Separation based on size difference is common, but there are other ways to either block one component or enhance transport of another through a membrane. Based on increasing molecular understanding of both biological and synthetic membranes, key design criteria for new membranes have emerged: (i) properly sized free-volume elements (or pores), (ii) narrow free-volume element (or pore size) distribution, (iii) a thin active layer, and (iv) highly tuned interactions between permeants of interest and the membrane. Here, we discuss the permeability/selectivity trade-off, highlight similarities and differences between synthetic and biological membranes, describe challenges for existing membranes, and identify fruitful areas of future research. ADVANCES Many organic, inorganic, and hybrid materials have emerged as potential membranes. In addition to polymers, used for most membranes today, materials such as carbon molecular sieves, ceramics, zeolites, various nanomaterials (e.g., graphene, graphene oxide, and metal organic frameworks), and their mixtures with polymers have been explored. Simultaneously, global challenges such as climate change and rapid population growth stimulate the search for efficient water purification and energy-generation technologies, many of which are membrane-based. Additional driving forces include wastewater reuse from shale gas extraction and improvement of chemical and petrochemical separation processes by increasing the use of light hydrocarbons for chemicals manufacturing. OUTLOOK Opportunities for advancing membranes include (i) more mechanically, chemically, and thermally robust materials; (ii) judiciously higher permeability and selectivity for applications where such improvements matter; and (iii) more emphasis on fundamental structure/property/processing relations. There is a pressing need for membranes with improved selectivity, rather than membranes with improved permeability, especially for water purification. Modeling at all length scales is needed to develop a coherent molecular understanding of membrane properties, provide insight for future materials design, and clarify the fundamental basis for trade-off behavior. Basic molecular-level understanding of thermodynamic and diffusion properties of water and ions in charged membranes for desalination and energy applications such as fuel cells is largely incomplete. Fundamental understanding of membrane structure optimization to control transport of minor species (e.g., trace-organic contaminants in desalination membranes, neutral compounds in charged membranes, and heavy hydrocarbons in membranes for natural gas separation) is needed. Laboratory evaluation of membranes is often conducted with highly idealized mixtures, so separation performance in real applications with complex mixtures is poorly understood. Lack of systematic understanding of methodologies to scale promising membranes from the few square centimeters needed for laboratory studies to the thousands of square meters needed for large applications stymies membrane deployment. Nevertheless, opportunities for membranes in both existing and emerging applications, together with an expanding set of membrane materials, hold great promise for membranes to effectively address separations needs.

Melanoma staging: Evidence-based changes in the American Joint Committee on Cancer eighth edition cancer staging manual.

Abstract: Answer questions and earn CME/CNE To update the melanoma staging system of the American Joint Committee on Cancer (AJCC) a large database was assembled comprising >46,000 patients from 10 centers worldwide with stages I, II, and III melanoma diagnosed since 1998. Based on analyses of this new database, the existing seventh edition AJCC stage IV database, and contemporary clinical trial data, the AJCC Melanoma Expert Panel introduced several important changes to the Tumor, Nodes, Metastasis (TNM) classification and stage grouping criteria. Key changes in the eighth edition AJCC Cancer Staging Manual include: 1) tumor thickness measurements to be recorded to the nearest 0.1 mm, not 0.01 mm; 2) definitions of T1a and T1b are revised (T1a, <0.8 mm without ulceration; T1b, 0.8-1.0 mm with or without ulceration or <0.8 mm with ulceration), with mitotic rate no longer a T category criterion; 3) pathological (but not clinical) stage IA is revised to include T1b N0 M0 (formerly pathologic stage IB); 4) the N category descriptors "microscopic" and "macroscopic" for regional node metastasis are redefined as "clinically occult" and "clinically apparent"; 5) prognostic stage III groupings are based on N category criteria and T category criteria (ie, primary tumor thickness and ulceration) and increased from 3 to 4 subgroups (stages IIIA-IIID); 6) definitions of N subcategories are revised, with the presence of microsatellites, satellites, or in-transit metastases now categorized as N1c, N2c, or N3c based on the number of tumor-involved regional lymph nodes, if any; 7) descriptors are added to each M1 subcategory designation for lactate dehydrogenase (LDH) level (LDH elevation no longer upstages to M1c); and 8) a new M1d designation is added for central nervous system metastases. This evidence-based revision of the AJCC melanoma staging system will guide patient treatment, provide better prognostic estimates, and refine stratification of patients entering clinical trials. CA Cancer J Clin 2017;67:472-492. © 2017 American Cancer Society.

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

Abstract: Additional co-authors: Endre Czeiter, Marek Czosnyka, Ramon Diaz-Arrastia, Jens P Dreier, Ann-Christine Duhaime, Ari Ercole, Thomas A van Essen, Valery L Feigin, Guoyi Gao, Joseph Giacino, Laura E Gonzalez-Lara, Russell L Gruen, Deepak Gupta, Jed A Hartings, Sean Hill, Ji-yao Jiang, Naomi Ketharanathan, Erwin J O Kompanje, Linda Lanyon, Steven Laureys, Fiona Lecky, Harvey Levin, Hester F Lingsma, Marc Maegele, Marek Majdan, Geoffrey Manley, Jill Marsteller, Luciana Mascia, Charles McFadyen, Stefania Mondello, Virginia Newcombe, Aarno Palotie, Paul M Parizel, Wilco Peul, James Piercy, Suzanne Polinder, Louis Puybasset, Todd E Rasmussen, Rolf Rossaint, Peter Smielewski, Jeannette Soderberg, Simon J Stanworth, Murray B Stein, Nicole von Steinbuchel, William Stewart, Ewout W Steyerberg, Nino Stocchetti, Anneliese Synnot, Braden Te Ao, Olli Tenovuo, Alice Theadom, Dick Tibboel, Walter Videtta, Kevin K W Wang, W Huw Williams, Kristine Yaffe for the InTBIR Participants and Investigators

ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee

Abstract: Thyroid nodules are a frequent finding on neck sonography. Most nodules are benign; therefore, many nodules are biopsied to identify the small number that are malignant or require surgery for a definitive diagnosis. Since 2009, many professional societies and investigators have proposed ultrasound-based risk stratification systems to identify nodules that warrant biopsy or sonographic follow-up. Because some of these systems were founded on the BI-RADS® classification that is widely used in breast imaging, their authors chose to apply the acronym TI-RADS, for Thyroid Imaging, Reporting and Data System. In 2012, the ACR convened committees to (1) provide recommendations for reporting incidental thyroid nodules, (2) develop a set of standard terms (lexicon) for ultrasound reporting, and (3) propose a TI-RADS on the basis of the lexicon. The committees published the results of the first two efforts in 2015. In this article, the authors present the ACR TI-RADS Committee's recommendations, which provide guidance regarding management of thyroid nodules on the basis of their ultrasound appearance. The authors also describe the committee's future directions.

Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies and the Distant Universe

Abstract: We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median $z\sim 0.03$). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between $z\sim 0.6$ and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July.

The Apache Point Observatory Galactic Evolution Experiment (APOGEE)

Abstract: National Science Foundation [AST-1109178, AST-1616636]; Gemini Observatory; Spanish Ministry of Economy and Competitiveness [AYA-2011-27754]; NASA [NNX12AE17G]; Hungarian Academy of Sciences; Hungarian NKFI of the Hungarian National Research, Development and Innovation Office [K-119517]; Alfred P. Sloan Foundation; National Science Foundation; U.S. Department of Energy Office of Science

2016 update of the ASAS-EULAR management recommendations for axial spondyloarthritis

Abstract: To update and integrate the recommendations for ankylosing spondylitis and the recommendations for the use of tumour necrosis factor inhibitors (TNFi) in axial spondyloarthritis (axSpA) into one set applicable to the full spectrum of patients with axSpA. Following the latest version of the European League Against Rheumatism (EULAR) Standardised Operating Procedures, two systematic literature reviews first collected the evidence regarding all treatment options (pharmacological and non-pharmacological) that were published since 2009. After a discussion of the results in the steering group and presentation to the task force, overarching principles and recommendations were formulated, and consensus was obtained by informal voting. A total of 5 overarching principles and 13 recommendations were agreed on. The first three recommendations deal with personalised medicine including treatment target and monitoring. Recommendation 4 covers non-pharmacological management. Recommendation 5 describes the central role of non-steroidal anti-inflammatory drugs (NSAIDs) as first-choice drug treatment. Recommendations 6-8 define the rather modest role of analgesics, and disprove glucocorticoids and conventional synthetic disease-modifying antirheumatic drugs (DMARDs) for axSpA patents with predominant axial involvement. Recommendation 9 refers to biological DMARDs (bDMARDs) including TNFi and IL-17 inhibitors (IL-17i) for patients with high disease activity despite the use (or intolerance/contraindication) of at least two NSAIDs. In addition, they should either have an elevated C reactive protein and/or definite inflammation on MRI and/or radiographic evidence of sacroiliitis. Current practice is to start with a TNFi. Switching to another TNFi or an IL-17i is recommended in case TNFi fails (recommendation 10). Tapering, but not stopping a bDMARD, can be considered in patients in sustained remission (recommendation 11). The final two recommendations (12, 13) deal with surgery and spinal fractures. The 2016 Assessment of SpondyloArthritis international Society-EULAR recommendations provide up-to-date guidance on the management of patients with axSpA.

Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning:

Abstract: Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology’s near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research ques...

Small-Scale Challenges to the $\Lambda$CDM Paradigm.

Abstract: The dark energy plus cold dark matter ($\Lambda$CDM) cosmological model has been a demonstrably successful framework for predicting and explaining the large-scale structure of Universe and its evolution with time. Yet on length scales smaller than $\sim 1$ Mpc and mass scales smaller than $\sim 10^{11} M_{\odot}$, the theory faces a number of challenges. For example, the observed cores of many dark-matter dominated galaxies are both less dense and less cuspy than naively predicted in $\Lambda$CDM. The number of small galaxies and dwarf satellites in the Local Group is also far below the predicted count of low-mass dark matter halos and subhalos within similar volumes. These issues underlie the most well-documented problems with $\Lambda$CDM: Cusp/Core, Missing Satellites, and Too-Big-to-Fail. The key question is whether a better understanding of baryon physics, dark matter physics, or both will be required to meet these challenges. Other anomalies, including the observed planar and orbital configurations of Local Group satellites and the tight baryonic/dark matter scaling relations obeyed by the galaxy population, have been less thoroughly explored in the context of $\Lambda$CDM theory. Future surveys to discover faint, distant dwarf galaxies and to precisely measure their masses and density structure hold promising avenues for testing possible solutions to the small-scale challenges going forward. Observational programs to constrain or discover and characterize the number of truly dark low-mass halos are among the most important, and achievable, goals in this field over then next decade. These efforts will either further verify the $\Lambda$CDM paradigm or demand a substantial revision in our understanding of the nature of dark matter.

A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification

Abstract: Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (Kim, 2014; Kalchbrenner et al., 2014; Johnson and Zhang, 2014; Zhang et al., 2016). However, these models require practitioners to specify an exact model architecture and set accompanying hyperparameters, including the filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance, which makes it a modern standard baseline method akin to Support Vector Machine (SVMs) and logistic regression. We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification in real world settings.

Scanning the horizon: towards transparent and reproducible neuroimaging research

Abstract: Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

Observation of a discrete time crystal

Abstract: Spontaneous symmetry breaking is a fundamental concept in many areas of physics, including cosmology, particle physics and condensed matter. An example is the breaking of spatial translational symmetry, which underlies the formation of crystals and the phase transition from liquid to solid. Using the analogy of crystals in space, the breaking of translational symmetry in time and the emergence of a 'time crystal' was recently proposed, but was later shown to be forbidden in thermal equilibrium. However, non-equilibrium Floquet systems, which are subject to a periodic drive, can exhibit persistent time correlations at an emergent subharmonic frequency. This new phase of matter has been dubbed a 'discrete time crystal'. Here we present the experimental observation of a discrete time crystal, in an interacting spin chain of trapped atomic ions. We apply a periodic Hamiltonian to the system under many-body localization conditions, and observe a subharmonic temporal response that is robust to external perturbations. The observation of such a time crystal opens the door to the study of systems with long-range spatio-temporal correlations and novel phases of matter that emerge under intrinsically non-equilibrium conditions.

Sampling Matters in Deep Embedding Learning

Abstract: Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.

Impending extinction crisis of the world's primates: why primates matter

Abstract: Nonhuman primates, our closest biological relatives, play important roles in the livelihoods, cultures, and religions of many societies and offer unique insights into human evolution, biology, behavior, and the threat of emerging diseases. They are an essential component of tropical biodiversity, contributing to forest regeneration and ecosystem health. Current information shows the existence of 504 species in 79 genera distributed in the Neotropics, mainland Africa, Madagascar, and Asia. Alarmingly, ~60% of primate species are now threatened with extinction and ~75% have declining populations. This situation is the result of escalating anthropogenic pressures on primates and their habitats—mainly global and local market demands, leading to extensive habitat loss through the expansion of industrial agriculture, large-scale cattle ranching, logging, oil and gas drilling, mining, dam building, and the construction of new road networks in primate range regions. Other important drivers are increased bushmeat hunting and the illegal trade of primates as pets and primate body parts, along with emerging threats, such as climate change and anthroponotic diseases. Often, these pressures act in synergy, exacerbating primate population declines. Given that primate range regions overlap extensively with a large, and rapidly growing, human population characterized by high levels of poverty, global attention is needed immediately to reverse the looming risk of primate extinctions and to attend to local human needs in sustainable ways. Raising global scientific and public awareness of the plight of the world’s primates and the costs of their loss to ecosystem health and human society is imperative.

A gravitational-wave standard siren measurement of the Hubble constant

Abstract: On 17 August 2017, the Advanced LIGO1 and Virgo2 detectors observed the gravitational-wave event GW170817—a strong signal from the merger of a binary neutron-star system3. Less than two seconds after the merger, a γ-ray burst (GRB 170817A) was detected within a region of the sky consistent with the LIGO–Virgo-derived location of the gravitational-wave source4, 5, 6. This sky region was subsequently observed by optical astronomy facilities7, resulting in the identification8, 9, 10, 11, 12, 13 of an optical transient signal within about ten arcseconds of the galaxy NGC 4993. This detection of GW170817 in both gravitational waves and electromagnetic waves represents the first ‘multi-messenger’ astronomical observation. Such observations enable GW170817 to be used as a ‘standard siren’14, 15, 16, 17, 18 (meaning that the absolute distance to the source can be determined directly from the gravitational-wave measurements) to measure the Hubble constant. This quantity represents the local expansion rate of the Universe, sets the overall scale of the Universe and is of fundamental importance to cosmology. Here we report a measurement of the Hubble constant that combines the distance to the source inferred purely from the gravitational-wave signal with the recession velocity inferred from measurements of the redshift using the electromagnetic data. In contrast to previous measurements, ours does not require the use of a cosmic ‘distance ladder’19: the gravitational-wave analysis can be used to estimate the luminosity distance out to cosmological scales directly, without the use of intermediate astronomical distance measurements. We determine the Hubble constant to be about 70 kilometres per second per megaparsec. This value is consistent with existing measurements20, 21, while being completely independent of them. Additional standard siren measurements from future gravitational-wave sources will enable the Hubble constant to be constrained to high precision.

High-voltage positive electrode materials for lithium-ion batteries

Abstract: The ever-growing demand for advanced rechargeable lithium-ion batteries in portable electronics and electric vehicles has spurred intensive research efforts over the past decade. The key to sustaining the progress in Li-ion batteries lies in the quest for safe, low-cost positive electrode (cathode) materials with desirable energy and power capabilities. One approach to boost the energy and power densities of batteries is to increase the output voltage while maintaining a high capacity, fast charge–discharge rate, and long service life. This review gives an account of the various emerging high-voltage positive electrode materials that have the potential to satisfy these requirements either in the short or long term, including nickel-rich layered oxides, lithium-rich layered oxides, high-voltage spinel oxides, and high-voltage polyanionic compounds. The key barriers and the corresponding strategies for the practical viability of these cathode materials are discussed along with the optimization of electrolytes and other cell components, with a particular emphasis on recent advances in the literature. A concise perspective with respect to plausible strategies for future developments in the field is also provided.

Janus Monolayer Transition-Metal Dichalcogenides

Abstract: The crystal configuration of sandwiched S–Mo–Se structure (Janus SMoSe) at the monolayer limit has been synthesized and carefully characterized in this work. By controlled sulfurization of monolayer MoSe2, the top layer of selenium atoms is substituted by sulfur atoms, while the bottom selenium layer remains intact. The structure of this material is systematically investigated by Raman, photoluminescence, transmission electron microscopy, and X-ray photoelectron spectroscopy and confirmed by time-of-flight secondary ion mass spectrometry. Density functional theory (DFT) calculations are performed to better understand the Raman vibration modes and electronic structures of the Janus SMoSe monolayer, which are found to correlate well with corresponding experimental results. Finally, high basal plane hydrogen evolution reaction activity is discovered for the Janus monolayer, and DFT calculation implies that the activity originates from the synergistic effect of the intrinsic defects and structural strain inhe...

Evolving Deep Neural Networks

Abstract: The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand. This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods to topology, components, and hyperparameters, this method achieves results comparable to best human designs in standard benchmarks in object recognition and language modeling. It also supports building a real-world application of automated image captioning on a magazine website. Given the anticipated increases in available computing power, evolution of deep networks is promising approach to constructing deep learning applications in the future.

An Outlook on Lithium Ion Battery Technology

Abstract: Lithium ion batteries as a power source are dominating in portable electronics, penetrating the electric vehicle market, and on the verge of entering the utility market for grid-energy storage. Depending on the application, trade-offs among the various performance parameters—energy, power, cycle life, cost, safety, and environmental impact—are often needed, which are linked to severe materials chemistry challenges. The current lithium ion battery technology is based on insertion-reaction electrodes and organic liquid electrolytes. With an aim to increase the energy density or optimize the other performance parameters, new electrode materials based on both insertion reaction and dominantly conversion reaction along with solid electrolytes and lithium metal anode are being intensively pursued. This article presents an outlook on lithium ion technology by providing first the current status and then the progress and challenges with the ongoing approaches. In light of the formidable challenges with some of the...

Long-Term Recurrent Convolutional Networks for Visual Recognition and Description

Abstract: Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent are effective for tasks involving sequences, visual and otherwise. We describe a class of recurrent convolutional architectures which is end-to-end trainable and suitable for large-scale visual understanding tasks, and demonstrate the value of these models for activity recognition, image captioning, and video description. In contrast to previous models which assume a fixed visual representation or perform simple temporal averaging for sequential processing, recurrent convolutional models are “doubly deep” in that they learn compositional representations in space and time. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Differentiable recurrent models are appealing in that they can directly map variable-length inputs (e.g., videos) to variable-length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent sequence models are directly connected to modern visual convolutional network models and can be jointly trained to learn temporal dynamics and convolutional perceptual representations. Our results show that such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined or optimized.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Abstract: Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

Asgard archaea illuminate the origin of eukaryotic cellular complexity

Abstract: The origin and cellular complexity of eukaryotes represent a major enigma in biology. Current data support scenarios in which an archaeal host cell and an alphaproteobacterial (mitochondrial) endosymbiont merged together, resulting in the first eukaryotic cell. The host cell is related to Lokiarchaeota, an archaeal phylum with many eukaryotic features. The emergence of the structural complexity that characterizes eukaryotic cells remains unclear. Here we describe the 'Asgard' superphylum, a group of uncultivated archaea that, as well as Lokiarchaeota, includes Thor-, Odin- and Heimdallarchaeota. Asgard archaea affiliate with eukaryotes in phylogenomic analyses, and their genomes are enriched for proteins formerly considered specific to eukaryotes. Notably, thorarchaeal genomes encode several homologues of eukaryotic membrane-trafficking machinery components, including Sec23/24 and TRAPP domains. Furthermore, we identify thorarchaeal proteins with similar features to eukaryotic coat proteins involved in vesicle biogenesis. Our results expand the known repertoire of 'eukaryote-specific' proteins in Archaea, indicating that the archaeal host cell already contained many key components that govern eukaryotic cellular complexity.

Modeling and Analyzing Millimeter Wave Cellular Systems

Abstract: We provide a comprehensive overview of mathematical models and analytical techniques for millimeter wave (mmWave) cellular systems. The two fundamental physical differences from conventional sub-6-GHz cellular systems are: 1) vulnerability to blocking and 2) the need for significant directionality at the transmitter and/or receiver, which is achieved through the use of large antenna arrays of small individual elements. We overview and compare models for both of these factors, and present a baseline analytical approach based on stochastic geometry that allows the computation of the statistical distributions of the downlink signal-to-interference-plus-noise ratio (SINR) and also the per link data rate, which depends on the SINR as well as the average load. There are many implications of the models and analysis: 1) mmWave systems are significantly more noise-limited than at sub-6 GHz for most parameter configurations; 2) initial access is much more difficult in mmWave; 3) self-backhauling is more viable than in sub-6-GHz systems, which makes ultra-dense deployments more viable, but this leads to increasingly interference-limited behavior; and 4) in sharp contrast to sub-6-GHz systems cellular operators can mutually benefit by sharing their spectrum licenses despite the uncontrolled interference that results from doing so. We conclude by outlining several important extensions of the baseline model, many of which are promising avenues for future research.