To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
This volume has achieved a large coverage of the experimentally well-studied areas of the temperate and subtropical coasts of the world (see Figure 1.1) – venturing into the tropics in some regions (Chapter 14, South-East Asia) and including mangroves (Chapter 17). Coral reef systems have not been considered. Much of the emphasis has been on rocky habitats as this is where the majority of experimental work on interactions has been done (but see Chapter 6). As well as reviewing regions where there has been a long history of experimental research (e.g., Chapters 2–4, 6, 10, 11, 13, 15, 16), areas of emerging experimental research in the last twenty-five years (e.g., Chapter 8, western Mediterranean; Chapter 12, south-east Pacific) and understudied regions (e.g., Chapter 7, Argentina; Chapter 14, South-East Asia) have also been included, allowing more comprehensive insights into the processes important for shaping these communities. In this short synthesis chapter, we first consider the main processes determining patterns covered by the previous chapters. We then consider major human impacts in these regions. Finally, we identify gaps in knowledge and make some suggestions for the way forward. We make the case for combining phylogeographic studies with macro-ecology and biogeography, coupled with well-designed hypothesis testing experiments, to better understand processes generating patterns on micro-evolutionary (hundreds to thousands of years) and ecological (up to hundreds of years) time scales.
For most common infections requiring hospitalization, antibiotic treatment is completed after hospital discharge. Postdischarge therapy is often unnecessarily broad spectrum and prolonged. We developed an intervention to improve antibiotic selection and shorten treatment durations.
Single center, quasi-experimental retrospective cohort study
Patients prescribed oral antibiotics at hospital discharge before (July 2012–June 2013) and after (October 2014–February 2015) an intervention consisting of (1) institutional guidance for oral step-down antibiotic selection and duration of therapy and (2) pharmacy audit of discharge prescriptions with real-time prescribing recommendations to providers. The primary outcomes measured were total prescribed duration of therapy and use of antibiotics with broad gram-negative activity (ie, fluoroquinolones or amoxicillin-clavulanate).
Overall, 300 cases from the preintervention period and 200 cases from the intervention period were included. Compared with the preintervention period, the use of antibiotics with broad gram-negative activity decreased during the intervention (51% vs 40%; P=.02), particularly fluoroquinolones (38% vs 25%; P=.002). The total duration of therapy decreased from a median of 10 days (interquartile range [IQR], 7–13 days) to 9 days (IQR, 6–13 days) but did not reach statistical significance (P=.13). However, the duration prescribed at discharge declined from 6 days (IQR, 4–10 days) to 5 days (IQR, 3–7 days) (P=.003). During the intervention, there was a nonsignificant increase in the overall appropriateness of discharge prescriptions from 52% to 66% (P=.15).
A multifaceted intervention to optimize antibiotic prescribing at hospital discharge was associated with less frequent use of antibiotics with broad gram-negative activity and shorter postdischarge treatment durations.
To evaluate changes in outpatient fluoroquinolone (FQ) and nitrofurantoin (NFT) use and resistance among E. coli isolates after a change in institutional guidance to use NFT over FQs for acute uncomplicated cystitis.
We compared 2 time periods: January 2003–June 2007 when FQs were recommended as first-line therapy, and July 2007–December 2012, when NFT was recommended. The main outcomes were changes in FQ and NFT use and FQ- and NFT-resistant E. coli by time-series analysis.
Overall, 5,714 adults treated for acute cystitis and 11,367 outpatient E. coli isolates were included in the analysis. After the change in prescribing guidance, there was an immediate 26% (95% CI, 20%–32%) decrease in FQ use (P<.001), and a nonsignificant 6% (95% CI, −2% to 15%) increase in NFT use (P=.12); these changes were sustained over the postintervention period. Oral cephalosporin use also increased during the postintervention period. There was a significant decrease in FQ-resistant E. coli of −0.4% per quarter (95% CI, −0.6% to −0.1%; P=.004) between the pre- and postintervention periods; however, a change in the trend of NFT-resistant E. coli was not observed.
In an integrated healthcare system, a change in institutional guidance for acute uncomplicated cystitis was associated with a reduction in FQ use, which may have contributed to a stabilization in FQ-resistant E. coli. Increased nitrofurantoin use was not associated with a change in NFT resistance.
Although the Great Basin of North America has produced some of the most robust and ancient fiber artifact assemblages in the world, many were recovered with poor chronological controls. Consequently, this class of artifacts has seldom been effectively incorporated into general discussions of early chronological and cultural patterns. In recent years, the Great Basin Textile Dating Project has accumulated direct AMS dates on textiles (bags, sandals, mats, cordage, and basketry) from dry caves in the Great Basin, particularly in the northern and western areas. We focus here on the terminal Pleistocene/early Holocene, to identify chronological patterns in this class of artifacts and to evaluate Adovasio’s characterization of the region’s earliest basketry as simple and undecorated. New AMS dates now suggest that the region’s earliest people had sophisticated textile traditions that incorporated numerous decorative elaborations. Some distinctive structures, including Fort Rock sandals and weft-faced plaited textiles, have limited early temporal ranges and may serve as diagnostic indicators for terminal Pleistocene/early Holocene times. Other basketry forms and structures that appear by about 9000 cal B.P. persist into the historic period, suggesting a stronger thread of continuity (especially in the north) from this time than is apparent in lithic traditions
We conducted a time-series analysis to evaluate the impact of the ASP over a 6.25-year period (July 1, 2008–September 30, 2014) while controlling for trends during a 3-year preintervention period (July 1, 2005–June 30, 2008). The primary outcome measures were total antibacterial and antipseudomonal use in days of therapy (DOT) per 1,000 patient-days (PD). Secondary outcomes included antimicrobial costs and resistance, hospital-onset Clostridium difficile infection, and other patient-centered measures.
During the preintervention period, total antibacterial and antipseudomonal use were declining (−9.2 and −5.5 DOT/1,000 PD per quarter, respectively). During the stewardship period, both continued to decline, although at lower rates (−3.7 and −2.2 DOT/1,000 PD, respectively), resulting in a slope change of 5.5 DOT/1,000 PD per quarter for total antibacterial use (P=.10) and 3.3 DOT/1,000 PD per quarter for antipseudomonal use (P=.01). Antibiotic expenditures declined markedly during the stewardship period (−$295.42/1,000 PD per quarter, P=.002). There were variable changes in antimicrobial resistance and few apparent changes in C. difficile infection and other patient-centered outcomes.
In a hospital with low baseline antibiotic use, implementation of an ASP was associated with sustained reductions in total antibacterial and antipseudomonal use and declining antibiotic expenditures. Common ASP outcome measures have limitations.
Of 300 patients prescribed oral antibiotics at the time of hospital discharge, urinary tract infection, community-acquired pneumonia, and skin infections accounted for 181 of the treatment indications (60%). Half of the prescriptions were antibiotics with broad Gram-negative activity. Discharge prescriptions were inappropriate in 79 of 150 cases reviewed (53%).
Hospitalizations for acute bacterial skin and skin structure infection (ABSSSI) are common. Optimizing antibiotic use for ABSSSIs requires an understanding of current management. The objective of this study was to evaluate antibiotic prescribing practices and factors affecting prescribing in a diverse group of hospitals
Multicenter, retrospective cohort study.
Seven community and academic hospitals.
Children and adults hospitalized between June 2010 and May 2012 for cellulitis, wound infection, or cutaneous abscess were eligible. The primary endpoint was a composite of 2 prescribing practices representing potentially avoidable antibiotic exposure: (1) use of antibiotics with a broad spectrum of activity against gram-negative bacteria or (2) treatment duration greater than 10 days.
A total of 533 cases were included: 320 with nonpurulent cellulitis, 44 with wound infection or purulent cellulitis, and 169 with abscess. Of 492 cases with complete prescribing data, the primary endpoint occurred in 394 (80%) cases and varied significantly across hospitals (64%–97%; P < .001). By logistic regression, independent predictors of the primary endpoint included wound infection or purulent cellulitis (odds ratio [OR], 5.12 [95% confidence interval (CI)], 1.46–17.88), head or neck involvement (OR, 2.83 [95% CI, 1.17–6.82]), adult cases (OR, 2.20 [95% CI, 1.18–4.11]), and admission to a community hospital (OR, 1.90 [95% CI, 1.05–3.44]).
Among patients hospitalized for ABSSSI, use of antibiotics with broad gram-negative activity or treatment courses longer than 10 days were common. There may be substantial opportunity to reduce antibiotic exposure through shorter courses of therapy targeting gram-positive bacteria.
Infect Control Hosp Epidemiol 2014;35(10):1241–1250
Compound specific radiocarbon measurements can be made instantaneously using a gas chromatograph (GC) combustion system coupled to a 14C AMS system fitted with a gas ion source. Samples below 10 μg C can be analyzed but the precision is reduced to 5–10% because of lower source efficiency. We modified our GC for CH4 and CO2 analysis and injected samples multiple times to sum data and increase precision. We attained a maximum precision of 0.6% for modern CO2 from 25 injections of 27 μg C and a background of ≃0.5% (40 kyr) for ancient methane. The 14C content of dissolved CO2 and CH4 in water samples collected at a deep-sea hydrothermal vent and a serpentine mud volcano was measured and the results for the vent sample are consistent with previously published data. Further experiments are required to determine a calibration and correction procedure to maximize accuracy.
The Kepler photometer was launched in March 2009 initiating NASA's search for Earth-size planets orbiting in the habitable zone of their star. After three years of science operations, Kepler has proven to be a veritable cornucopia of science results, both for exoplanets and for astrophysics. The phenomenal photometric precision and continuous observations required in order to identify small, rocky transiting planets enables the study of a large range of phenomena contributing to stellar variability for many thousands of solar-like stars in Kepler's field of view in exquisite detail. These effects range from <1 ppm acoustic oscillations on timescales from a few minutes and longward, to flares on timescales of hours, to spot-induced modulation on timescales of days to weeks to activity cycles on timescales of months to years. Recent improvements to the science pipeline have greatly enhanced Kepler's ability to reject instrumental signatures while better preserving intrinsic stellar variability, opening up the timescales for study well beyond 10 days. We give an overview of the stellar variability we see across the full range of spectral types observed by Kepler, from the cool, small red M stars to the hot, large late A stars, both in terms of amplitude as well as timescale. We also present a picture of what the extended mission will likely bring to the field of stellar variability as we progress from a 3.5 year mission to a 7.5+ year mission.
Nothing so like as eggs; yet no one, on account of this appearing similarity, expects the same taste and relish in all of them.
Goals and examples of sequence analysis
Sequences of data, either in space or in time, appear all the time in ocean research. You may have a time series of measurements at a location (e.g. sediment trap data, or ocean surface temperature), a series of stations along a hydrographic section, or isotope measurements on a long sediment core. For the sake of simplicity (initially) we shall discuss only regularly sampled data; that is, samples taken at identical intervals in space or time. The analysis becomes more difficult and more complicated when we discuss irregularly spaced samples, but the principles are similar and best understood in terms of the simplest case first. What do we hope to achieve in the analysis of data sequences? There are as many reasons (or perhaps more) as there are data sequences. The next subsections outline briefly some of the major conceptual motivations.
Searching or testing for structure or periodicities
Within a single data set you might be looking or testing for changes in a system due to periodic forcing, for example the effect of seasonal changes on biological production, or the effect of lunar tides on shell-fish contamination. This may be extended to spatial regularity as well, in that you may be looking for evidence of large-scale Kelvin waves (rapidly propagating variations of the thermocline depth) on dissolved nutrients near convergence zones in the ocean.
Answering difficult questions is always easier than answering easy ones: you are not accountable for the inconsistencies. And asking simple questions is the hardest part of all.
Until now we have concentrated on what may be loosely termed “data analysis methods”. In some respects, this is a form of modeling in that we are attempting to interpret our data within the context of some intrinsic model of how our data should behave, whether it be assuming the data follow an underlying probability distribution, vary as a function of some other variables, or exhibit some periodic behavior as a function of time. We hope you are beginning to see that all of these methods share common mathematical and algorithmic roots, and we want you to realize that many of these tools will come in handy as we now embark on a more model-intensive course.
Before doing so, we want to outline the basic aspects of model design, implementation, and analysis. Selecting the most accurate and efficient algorithms and developing robust and usable MATLAB code is important, but most of your intellectual energies should be directed at the design and analysis steps. Moreover, although correct design is vital to any successful modeling effort, developing the tools to efficiently analyze model output is just as important. It is critical to assessing the mechanics of how a model is performing as well as ultimately understanding the underlying system dynamics and how well a model compares to observations.
It is better to take many small steps in the right direction than to make a great leap forward only to stumble backward.
Ancient Chinese Proverb
Everything should be made as simple as possible, but not simpler.
Constructing numerical models of marine systems usually involves setting up a series of partial differential equations, specifying boundary conditions and then “running the model”. Your purpose may be to establish the value of parameters (e.g. rates of reaction or the magnitude of some property), estimate fluxes, or make some prediction about the future state of the system. Although you can sometimes choose a physical problem that is simple enough to be modeled with analytic solutions (an example would be Munk's 1966 “Abyssal recipes” model; Chapter 13), more often than not you will encounter situations where the processes or the geometry of the system are too complex to allow analytic solutions.
Don't get us wrong; analytic solutions are nice. They can often provide you with a nice conceptual, intuitive feel for how the system responds, especially in an asymptotic sense. However, for realistic geometries, you will find that the few analytical solutions provided in many books are infinite series solutions. Be very, very careful when dealing with those series solutions. Pay particular attention to the assumptions made in deriving the solutions, to the conditions under which they ought to be applied, and especially to convergence issues.