Chip-Scale Nanophotonic Chemical and Biological Sensors Using CMOS Process

A monolithic integrated chip-scale surface plasmon resonance (SPR) sensor is demonstrated. The device consists of a pn photodiode covered with a periodic modified thin metal film whose lattice constant is on the order of the wavelength of light. The device performs real-time measurement of resonant wavelengths of enhanced optical transmission due to surface plasmon resonance, which are influenced by the presence of chemical or biological materials at the device’s surface

Observation of sub-Bragg diffraction of waves in crystals

We investigate the diffraction conditions and associated formation of stopgaps for waves in crystals with different Bravais lattices. We identify a prominent stopgap in high-symmetry directions that occurs at a frequency below the ubiquitous first-order Bragg condition. This sub-Bragg diffraction condition is demonstrated by reflectance spectroscopy on two-dimensional photonic crystals with a centred rectangular lattice, revealing prominent diffraction peaks for both the sub-Bragg and first-order Bragg condition. These results have implications for wave propagation in 2 of the 5 two-dimensional Bravais lattices and 7 out of 14 three-dimensional Bravais lattices, such as centred rectangular, triangular, hexagonal and body-centred cubic

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of \u3e0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes

Impaired natural killer cell phenotype and function in idiopathic and heritable pulmonary arterial hypertension

BACKGROUND: Beyond their role as innate immune effectors, natural killer (NK) cells are emerging as important regulators of angiogenesis and vascular remodeling. Pulmonary arterial hypertension (PAH) is characterized by severe pulmonary vascular remodeling and has long been associated with immune dysfunction. Despite this association, a role for NK cells in disease pathology has not yet been described. METHODS AND RESULTS: Analysis of whole blood lymphocytes and isolated NK cells from PAH patients revealed an expansion of the functionally defective CD56(-)/CD16(+) NK subset that was not observed in patients with chronic thromboembolic pulmonary hypertension. NK cells from PAH patients also displayed decreased levels of the activating receptor NKp46 and the killer immunoglobulin-like receptors 2DL1/S1 and 3DL1, reduced secretion of the cytokine macrophage inflammatory protein-1β, and a significant impairment in cytolytic function associated with decreased killer immunoglobulin-like receptor 3DL1 expression. Genotyping patients (n=222) and controls (n=191) for killer immunoglobulin-like receptor gene polymorphisms did not explain these observations. Rather, we show that NK cells from PAH patients exhibit increased responsiveness to transforming growth factor-β, which specifically downregulates disease-associated killer immunoglobulin-like receptors. NK cell number and cytotoxicity were similarly decreased in the monocrotaline rat and chronic hypoxia mouse models of PAH, accompanied by reduced production of interferon-γ in NK cells from hypoxic mice. NK cells from PAH patients also produced elevated quantities of matrix metalloproteinase 9, consistent with a capacity to influence vascular remodeling. CONCLUSIONS: Our work is the first to identify an impairment of NK cells in PAH and suggests a novel and substantive role for innate immunity in the pathobiology of this disease

The Role of Machine Learning in Spine Surgery: The Future Is Now

The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over- or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically

Divalent Nonaqueous Metal-Air Batteries

In the field of secondary batteries, the growing diversity of possible applications for energy storage has led to the investigation of numerous alternative systems to the state-of-the-art lithium-ion battery. Metal-air batteries are one such technology, due to promising specific energies that could reach beyond the theoretical maximum of lithium-ion. Much focus over the past decade has been on lithium and sodium-air, and, only in recent years, efforts have been stepped up in the study of divalent metal-air batteries. Within this article, the opportunities, progress, and challenges in nonaqueous rechargeable magnesium and calcium-air batteries will be examined and critically reviewed. In particular, attention will be focused on the electrolyte development for reversible metal deposition and the positive electrode chemistries (frequently referred to as the “air cathode”). Synergies between two cell chemistries will be described, along with the present impediments required to be overcome. Scientific advances in understanding fundamental cell (electro)chemistry and electrolyte development are crucial to surmount these barriers in order to edge these technologies toward practical application.</jats:p

The Look-back Time Evolution of Far-Ultraviolet Flux from the Brightest Cluster Elliptical Galaxies at z < 0.2

We present the GALEX UV photometry of the elliptical galaxies in Abell clusters at moderate redshifts (z < 0.2) for the study of the look-back time evolution of the UV upturn phenomenon. The brightest elliptical galaxies (M_r < -22) in 12 remote clusters are compared with the nearby giant elliptical galaxies of comparable optical luminosity in the Fornax and Virgo clusters. The sample galaxies presented here appear to be quiescent without signs of massive star formation or strong nuclear activity, and show smooth, extended profiles in their UV images indicating that the far-UV (FUV) light is mostly produced by hot stars in the underlying old stellar population. Compared to their counterparts in nearby clusters, the FUV flux of cluster giant elliptical galaxies at moderate redshifts fades rapidly with ~ 2 Gyrs of look-back time, and the observed pace in FUV - V color evolution agrees reasonably well with the prediction from the population synthesis models where the dominant FUV source is hot horizontal-branch stars and their progeny. A similar amount of color spread (~ 1 mag) in FUV - V exists among the brightest cluster elliptical galaxies at z ~ 0.1, as observed among the nearby giant elliptical galaxies of comparable optical luminosity.Comment: Accepted for publication in the Special GALEX ApJ Supplement, December 200

The GALEX Ultraviolet Atlas of Nearby Galaxies

We present images, integrated photometry, and surface-brightness and color profiles for a total of 1034 nearby galaxies recently observed by the Galaxy Evolution Explorer (GALEX) satellite in its far-ultraviolet (FUV; λ_(eff) = 1516 Å) and near-ultraviolet (NUV; λ_(eff) = 2267 Å) bands. Our catalog of objects is derived primarily from the GALEX Nearby Galaxies Survey (NGS) supplemented by galaxies larger than 1' in diameter serendipitously found in these fields and in other GALEX exposures of similar of greater depth. The sample analyzed here adequately describes the distribution and full range of properties (luminosity, color, star formation rate [SFR]) of galaxies in the local universe. From the surface brightness profiles obtained we have computed asymptotic magnitudes, colors, and luminosities, along with the concentration indices C31 and C42. We have also morphologically classified the UV surface brightness profiles according to their shape. This data set has been complemented with archival optical, near-infrared, and far-infrared fluxes and colors. We find that the integrated (FUV − K) color provides robust discrimination between elliptical and spiral/irregular galaxies and also among spiral galaxies of different subtypes. Elliptical galaxies with brighter K-band luminosities (i.e., more massive) are redder in (NUV − K) color but bluer in (FUV − NUV) (a color sensitive to the presence of a strong UV upturn) than less massive ellipticals. In the case of the spiral/irregular galaxies our analysis shows the presence of a relatively tight correlation between the (FUV − NUV) color (or, equivalently, the slope of the UV spectrum, β) and the total infrared-to-UV ratio. The correlation found between (FUV − NUV) color and K-band luminosity (with lower luminosity objects being bluer than more luminous ones) can be explained as due to an increase in the dust content with galaxy luminosity. The images in this Atlas along with the profiles and integrated properties are publicly available through a dedicated Web page

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies