Enhanced recovery after surgery: the future of elective arthroplasty?

Summary: Enhanced recovery after surgery is a method of streamlining the patient journey pre-, intra- and post-operatively in order to account for foreseeable and unforeseeable barriers to recovery. Originally pioneered in general surgery, the technique has been adopted in other specialities, given its potential to minimise the duration of hospitalisation, hasten recovery and improve patient experience. Enhanced recovery programmes are of particular interest in orthopaedic surgery, where patients who often have multiple comorbidities could gain substantial benefits from more efficient management. This is particularly pertinent given the rising prevalence of age-related joint disease requiring arthroplasty: Enhanced Recovery is more economically and clinically efficient. Relevance: Enhanced recovery is a relatively novel - heterogeneously implemented – method of managing the surgical patient journey. Intrinsic to the success of such programmes is a thorough understanding of its components and close communication within the multidisciplinary team. Medical students’ understanding of what these protocols involve will significantly affect their management of foreseeable – and unforeseeable – barriers to success in elective surgical patients during clinical years and in their future practice. It is therefore essential that all medical students – whether they have an interest in a surgical career or not – have a grounding in the components of enhanced recovery, because such programmes will form part of their practice at some point in their careers. Take-home message: Enhanced recovery is a proactive intervention, which has been shown to be extremely effective across a number of surgical disciplines in reducing length of stay, whilst maximising postoperative outcomes. Trainees would benefit from a detailed knowledge of enhanced recovery programmes in order to provide a higher standard of care during their encounters with patients at every stage of the surgical pathway

Investigation of elemental analysis using neutron-capture gamma ray spectra

"September 1969.""Prepared for United States Department of the Interior Bureau of Mines Morgantown Research Center Morgantown, West Virginia."Also issued as a Ph. D. thesis, written by the first author and supervised by the second author, MIT, Dept. of Nuclear Engineering, 1969Includes bibliographical references (pages 307-312)This thesis evaluated the potential of neutron-capture gamma rays in elemental analysis. A large portion of the work was devoted to the development of a method for the analysis of weak peaks in gamma ray spectra. This was based on equations developed for the standard deviation in the measurement of the various peak parameters, consideration being also given to the reduction in the statistical fluctuations obtained by smoothing the data with the use of Fourier transforms. Two methods of peak area determination were considered end their relative effectiveness examined. An equation was then derived for the minimum weight of an element needed for reliable quantitative analysis. The equations were verified using both real and pseudo-experimental data constructed with the use of a computer. Experiments were carried out using the MIT Reactor with samples positioned La) in a high neutron flux next to the reactor tank (2xl01- n/sq.cm sec), and (b) in an external neutron beam facility of relatively lower but well thermalized flux (2xl0 n/sq.cm sec). Capture gamma ray spectra were obtained with a three-crystal system capable of operating in the free mode, the Compton suppression mode and as a pair spectrometer. The results were used to examine the relative analytical sensitivity of the internal and external sample arrangements and the various gamma detection modes. The minimum measurable weights of 75 elements were evaluated for a stainless steel sample. For these computations use was made of the listing of capture gamma ray spectra recently established by the MIT gamma spectroscopy group. 'In a majority of the cases the detection limits range between 0.1 percent and 10 percent. Equations were developed for extending the results to different samples and different. experimental arrangements.Contract no. H018089

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

UVUDF: Ultraviolet Imaging of the Hubble Ultradeep Field with Wide-field Camera 3

We present an overview of a 90-orbit Hubble Space Telescope treasury program to obtain near ultraviolet imaging of the Hubble Ultra Deep Field using the Wide Field Camera 3 UVIS detector with the F225W, F275W, and F336W filters. This survey is designed to: (i) Investigate the episode of peak star formation activity in galaxies at 1<z<2.5; (ii) Probe the evolution of massive galaxies by resolving sub-galactic units (clumps); (iii) Examine the escape fraction of ionizing radiation from galaxies at z~2-3; (iv) Greatly improve the reliability of photometric redshift estimates; and (v) Measure the star formation rate efficiency of neutral atomic-dominated hydrogen gas at z~1-3. In this overview paper, we describe the survey details and data reduction challenges, including both the necessity of specialized calibrations and the effects of charge transfer inefficiency. We provide a stark demonstration of the effects of charge transfer inefficiency on resultant data products, which when uncorrected, result in uncertain photometry, elongation of morphology in the readout direction, and loss of faint sources far from the readout. We agree with the STScI recommendation that future UVIS observations that require very sensitive measurements use the instrument's capability to add background light through a "post-flash". Preliminary results on number counts of UV-selected galaxies and morphology of galaxies at z~1 are presented. We find that the number density of UV dropouts at redshifts 1.7, 2.1, and 2.7 is largely consistent with the number predicted by published luminosity functions. We also confirm that the image mosaics have sufficient sensitivity and resolution to support the analysis of the evolution of star-forming clumps, reaching 28-29th magnitude depth at 5 sigma in a 0.2 arcsecond radius aperture depending on filter and observing epoch.Comment: Accepted A

Room-Temperature Quantum Bit Memory Exceeding One Second

Stable quantum bits, capable both of storing quantum information for macroscopic time scales and of integration inside small portable devices, are an essential building block for an array of potential applications. We demonstrate high-fidelity control of a solid-state qubit, which preserves its polarization for several minutes and features coherence lifetimes exceeding 1 second at room temperature. The qubit consists of a single $^{13}C$ nuclear spin in the vicinity of a nitrogen-vacancy color center within an isotopically purified diamond crystal. The long qubit memory time was achieved via a technique involving dissipative decoupling of the single nuclear spin from its local environment. The versatility, robustness, and potential scalability of this system may allow for new applications in quantum information science.Physic

Radiation Injury After a Nuclear Detonation: Medical Consequences and the Need for Scarce Resources Allocation

A 10-kiloton (kT) nuclear detonation within a US city could expose hundreds of thousands of people to radiation. The Scarce Resources for a Nuclear Detonation Project was undertaken to guide community planning and response in the aftermath of a nuclear detonation, when demand will greatly exceed available resources. This article reviews the pertinent literature on radiation injuries from human exposures and animal models to provide a foundation for the triage and management approaches outlined in this special issue. Whole-body doses \u3e2 Gy can produce clinically significant acute radiation syndrome (ARS), which classically involves the hematologic, gastrointestinal, cutaneous, and cardiovascular/central nervous systems. The severity and presentation of ARS are affected by several factors, including radiation dose and dose rate, interindividual variability in radiation response, type of radiation (eg, gamma alone, gamma plus neutrons), partial-body shielding, and possibly age, sex, and certain preexisting medical conditions. The combination of radiation with trauma, burns, or both (ie, combined injury) confers a worse prognosis than the same dose of radiation alone. Supportive care measures, including fluid support, antibiotics, and possibly myeloid cytokines (eg, granulocyte colony-stimulating factor), can improve the prognosis for some irradiated casualties. Finally, expert guidance and surge capacity for casualties with ARS are available from the Radiation Emergency Medical Management Web site and the Radiation Injury Treatment Network

Emissions pathways, climate change, and impacts on California

The magnitude of future climate change depends substantially on the greenhouse gas emission pathways we choose. Here we explore the implications of the highest and lowest Intergovernmental Panel on Climate Change emissions pathways for climate change and associated impacts in California. Based on climate projections from two state-of-the-art climate models with low and medium sensitivity (Parallel Climate Model and Hadley Centre Climate Model, version 3, respectively), we find that annual temperature increases nearly double from the lower B1 to the higher A1fi emissions scenario before 2100. Three of four simulations also show greater increases in summer temperatures as compared with winter. Extreme heat and the associated impacts on a range of temperature-sensitive sectors are substantially greater under the higher emissions scenario, with some interscenario differences apparent before midcentury. By the end of the century under the B1 scenario, heatwaves and extreme heat in Los Angeles quadruple in frequency while heat-related mortality increases two to three times; alpine subalpine forests are reduced by 50–75%; and Sierra snowpack is reduced 30–70%. Under A1fi, heatwaves in Los Angeles are six to eight times more frequent, with heat-related excess mortality increasing five to seven times; alpine subalpine forests are reduced by 75–90%; and snowpack declines 73–90%, with cascading impacts on runoff and streamflow that, combined with projected modest declines in winter precipitation, could fundamentally disrupt California’s water rights system. Although interscenario differences in climate impacts and costs of adaptation emerge mainly in the second half of the century, they are strongly dependent on emissions from preceding decades

A stromal lysolipid-autotaxin signaling axis promotes pancreatic tumor progression

Pancreatic ductal adenocarcinoma (PDAC) develops a pronounced stromal response reflecting an aberrant wound-healing process. This stromal reaction features transdifferentiation of tissue-resident pancreatic stellate cells (PSC) into activated cancer-associated fibroblasts, a process induced by PDAC cells but of unclear significance for PDAC progression. Here, we show that PSCs undergo a dramatic lipid metabolic shift during differentiation in the context of pancreatic tumorigenesis, including remodeling of the intracellular lipidome and secretion of abundant lipids in the activated, fibroblastic state. Specifically, stroma-derived lysophosphatidylcholines support PDAC cell synthesis of phosphatidylcholines, key components of cell membranes, and also facilitate production of the potent wound-healing mediator lysophosphatidic acid (LPA) by the extracellular enzyme autotaxin, which is overexpressed in PDAC. The autotaxin–LPA axis promotes PDAC cell proliferation, migration, and AKT activation, and genetic or pharmacologic autotaxin inhibition suppresses PDAC growth in vivo. Our work demonstrates how PDAC cells exploit the local production of wound-healing mediators to stimulate their own growth and migration. Significance: Our work highlights an unanticipated role for PSCs in producing the oncogenic LPA signaling lipid and demonstrates how PDAC tumor cells co-opt the release of wound-healing mediators by neighboring PSCs to promote their own proliferation and migration