Miracle-dz/UniFAR-FTUs huggingface dataset:
The Not-So-Good Prognosis of Streptococcal Periprosthetic Joint Infection Managed by Implant Retention: The Results of a Large Multicenter Study

Background. Streptococci are not an infrequent cause of periprosthetic joint infection (PJI). Management by debridement, antibiotics, and implant retention (DAIR) is thought to produce a good prognosis, but little is known about the real likelihood of success. Methods. A retrospective, observational, multicenter, international study was performed during 2003-2012. Eligible patients had a streptococcal PJI that was managed with DAIR. The primary endpoint was failure, defined as death related to infection, relapse/persistence of infection, or the need for salvage therapy. Results. Overall, 462 cases were included (median age 72 years, 50% men). The most frequent species was Streptococcus agalactiae (34%), and 52% of all cases were hematogenous. Antibiotic treatment was primarily using β-lactams, and 37% of patients received rifampin. Outcomes were evaluable in 444 patients: failure occurred in 187 (42.1%; 95% confidence interval, 37.5%-46.7%) after a median of 62 days from debridement; patients without failure were followed up for a median of 802 days. Independent predictors (hazard ratios) of failure were rheumatoid arthritis (2.36), late post-surgical infection (2.20), and bacteremia (1.69). Independent predictors of success were exchange of removable components (0.60), early use of rifampin (0.98 per day of treatment within the first 30 days), and long treatments (≥21 days) with β-lactams, either as monotherapy (0.48) or in combination with rifampin (0.34). Conclusions. This is the largest series to our knowledge of streptococcal PJI managed by DAIR, showing a worse prognosis than previously reported. The beneficial effects of exchanging the removable components and of β-lactams are confirmed and maybe also a potential benefit from adding rifampin.
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes

Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks. Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model. In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference. Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm's operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference. We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation.
Interactions between the concentration of non-starch polysaccharides in wheat and the addition of an enzyme mixture in a broiler digestibility and performance trial

ABSTRACT Two broiler trials were designed to investigate the relationship between the concentration of non‐starch polysaccharides (NSP) in wheat and 1) its nutritional value for broilers and 2) the efficacy of exogenous enzymes. In a balance trial, diets were formulated with 3 wheat cultivars (Rustic and Viscount—medium NSP, Centenaire—high NSP) and were tested with or without the addition of an exogenous enzyme mixture. The diets were fed to 144 male Ross 308 broiler chickens housed in digestibility cages. Total tract nutrient digestibilities and AMEn were measured from 18 to 22 d of age. In a performance trial, diets were formulated with wheat (medium NSP diet) or with wheat mixed with rye and barley (high NSP diet) and were tested with or without the addition of an exogenous enzyme mixture. The diets were fed to 960 male Ross 308 broilers housed in pens and broiler performance during starter, grower and finisher periods was measured. In the balance trial, wheat cultivar did not affect nutrient digestibility or AMEn. Enzyme addition caused a significant increase in nutrient digestibilities and AMEn for the diet formulated with the high NSP wheat Centenaire only. In the performance trial, feeding the high NSP diet resulted in a higher feed conversion ratio and lower final body weight compared to the medium NSP diet. The largest improvements by enzyme addition were observed in the high NSP diet. In conclusion, the study was not able to show a consistent relationship between the NSP concentration of wheat and its nutritional value, but did demonstrate that the effect of an enzyme mixture on nutrient digestibility or broiler performance depends upon the NSP concentration in the diet.
Responsiveness of the Electrically Stimulated Cochlear Nerve in Children With Cochlear Nerve Deficiency

Objectives: This study aimed to (1) investigate the responsiveness of the cochlear nerve (CN) to a single biphasic-electrical pulse in implanted children with cochlear nerve deficiency (CND) and (2) compare their results with those measured in implanted children with normal-size CNs. Design: Participants included 23 children with CND (CND1 to CND23) and 18 children with normal-size CNs (S1 to S18). All subjects except for CND1 used Cochlear Nucleus cochlear implants with contour electrode arrays in their test ears. CND1 was implanted with a Cochlear Nucleus Freedom cochlear implant with a straight electrode array in the test ear. For each subject, the CN input/output (I/O) function and the refractory recovery function were measured using electrophysiological measures of the electrically evoked compound action potential (eCAP) at multiple electrode sites across the electrode array. Dependent variables included eCAP threshold, the maximum eCAP amplitude, slope of the I/O function, and time-constants of the refractory recovery function. Slopes of I/O functions were estimated using statistical modeling with a sigmoidal function. Recovery time-constants, including measures of the absolute refractory period and the relative refractory period, were estimated using statistical modeling with an exponential decay function. Generalized linear mixed-effect models were used to evaluate the effects of electrode site on the dependent variables measured in children with CND and to compare results of these dependent variables between subject groups. Results: The eCAP was recorded at all test electrodes in children with normal-size CNs. In contrast, the eCAP could not be recorded at any electrode site in 4 children with CND. For all other children with CND, the percentage of electrodes with measurable eCAPs decreased as the stimulating site moved in a basal-to-apical direction. For children with CND, the stimulating site had a significant effect on the slope of the I/O functions and the relative refractory period but showed no significant effect on eCAP threshold and the maximum eCAP amplitude. Children with CND had significantly higher eCAP thresholds, smaller maximum eCAP amplitudes, flatter slopes of I/O functions, and longer absolute refractory periods than children with normal-size CNs. There was no significant difference in the relative refractory period measured in these two subject groups. Conclusions: In children with CND, the functional status of the CN varied along the length of the cochlea. Compared with children with normal-size CNs, children with CND showed reduced CN responsiveness to electrical stimuli. The prolonged CN absolute refractory period in children with CND might account for, at least partially, the observed benefit of using relatively slow pulse rate in these patients.
Human scarred vocal folds 1 Histologic Characterization of Human Scarred Vocal Folds ( Abbreviation : Human scarred vocal folds )

Objectives: Vocal fold scarring remains a significant problem. Although several animal models have been developed to improve our understanding of the histopathology, the histologic features of scarred human vocal folds have rarely been reported. The present case studies aimed to define the histologic changes of scarred human vocal folds caused by cordectomy or cordotomy. Methods: Ten patients with the scarred vocal folds were involved in this study. Nine patients with early glottic cancer underwent endoscopic cordectomy, and one patient underwent superficial cordotomy for idiopathic scar. The post-cordectomy or cordotomy scar was biopsied or resected 3 to 13 months after the original procedure. After confirming absence of any tumor in cancer patients, the remaining specimens were used in the present study. Histologic examination investigated deposition of extracellular matrix (ECM) including collagen, elastin, hyaluronic acid (HA), fibronectin, and decorin in the lamina propria of the scarred vocal folds. Results: There was a wide range variation in the deposition of ECM in scarred vocal folds. Excessive and disorganized collagen deposition was observed in most cases that had undergone deep resection of the lamina propria, whereas deposition of collagen was mild and well organized after superficial resection. Decorin was retained in all cases after superficial cordectomy or cordotomy, but varied after deep resection. Deposition of elastin, HA, and fibronectin varied regardless of depth of injury. Conclusion: Histology of scarred vocal folds may vary with degree of injury and individual healing mechanism.
Doubly robust estimation of causal effects.

Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal's Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc. edu/~mfunk/dr/.
Novel Multivariate Analysis for Soil Carbon Measurements Using Laser‐Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS), a rapid and potentially field-deployable technology for estimating total carbon in soil, represents a novel approach to address important issues in soil science and carbon management. Our study has shown that models relating LIBS signal intensity at 247.85 nm to percent total carbon determined by dry combustion vary as a function of elemental and textural characteristics of the soil, and, to a lesser extent, wavelength and excitation energy of the laser. To better quantify these sources of variation, two wavelengths and three excitation energies were used to analyze soils from various locations. The emission line of carbon at 247.85 nm was pronounced at an excitation wavelength of 532 nm and energy of 45 mJ, but it was largely obscured by the 248.9 nm Fe line at 1064 nm and excitation energies of 90 and 135 mJ. Univariate analysis revealed linear, but soil-specific correlations between signal intensity at 247.85 nm and total carbon concentration. A single calibration model correlating LIBS spectra to carbon concentration in all samples was obtained using a multivariate approach. Several emission lines in addition to the strong carbon line contributed significantly to the multivariate model. These results show that multivariate analysis can bemore » used to construct a robust calibration model for LIBS spectra and therein provide a reliable estimate of total soil carbon. Such results must be confirmed for a broader range of soils, yet crop and soil scientists, carbon managers, and instrument developers should find these results encouraging. « less
Extraction Method and Analysis of Cannabinoids in Cannabis Olive Oil Preparations

Abstract Recently, an increasing number of pharmacists had to supply medicinal products based on Cannabis sativa L. (Cannabaceae), prescribed by physicians to individual patients. Cannabis olive oil preparation is the first choice as a concentrated extract of cannabinoids, even though standardized operative conditions for obtaining it are still not available. In this work, the impact of temperature and extraction time on the concentration of active principles was studied to harmonize the different compounding methods, optimize the extraction process, and reduce the variability among preparations. Moreover, starting from the cannabis inflorescence, the effect of temperature on tetrahydrocannabinolic acid decarboxylation was evaluated. For the analysis, a GC/MS method, as suggested by the Italian Ministry of Health, and a GC/flame ionization detection method were developed, validated, and compared.
Persuasion in Global Games with Application to Stress Testing ⇤

We study robust/adversarial information design in global games of regime change. We consider both the case in which the designer is constrained to disclose the same information to all market participants, as well as the case in which discriminatory disclosures are possible. In both cases, we show that the optimal policy has the “ perfect coordination property ”: it coordinates all market participants on the same course of action. Importantly, while the optimal policy removes any “strategic uncertainty,” it preserves (and in some cases, it enhances) heterogeneity in “structural uncertainty”. Under the optimal policy, each agent can perfectly predict the actions of any other agent, but not the beliefs that rationalize such actions. When the designer is constrained to public disclosures, we identify conditions under which the optimal policy takes the form of a simple “pass/fail” test, as well as conditions under which the optimal test is monotone (i.e., it fails with certainty institutions with weak fundamentals and passes those with strong ones). We then discuss the beneﬁts of discriminatory disclosures and show that the latter come from the possibility to “divide-and-conquer” the market: by combining a pass/fail public announcement with discriminatory disclosures that enhance the dispersion of higher-order beliefs, the policy maker minimizes the risk of regime change, while coordinating the market on the same course of action.
Toward a better integration of biological data from precipitation manipulation experiments into Earth system models

The biological responses to precipitation within the terrestrial components of Earth system models, or land surface models (LSMs), are mechanistically simple and poorly constrained, leaving projections of terrestrial ecosystem functioning and feedbacks to climate change uncertain. A number of field experiments have been conducted or are underway to test how changing precipitation will affect terrestrial ecosystems. Results from these experiments have the potential to vastly improve modeled processes. However, the transformation of experimental results into model improvements still represents a grand challenge. Here we review the current state of precipitation manipulation experiments and the precipitation responses of biological processes in LSMs to explore how these experiments can help improve model realism. First, we discuss contemporary precipitation projections and then review the structure and function of current‐generation LSMs. We then examine different experimental designs and discuss basic variables that, if measured, would increase a field experiment's usefulness in a modeling context. Next, we compare biological processes commonly measured in the field with their model analogs and find that, in many cases, the way these processes are measured in the field is not compatible with the way they are represented in LSMs, an effect that hinders model development. We then discuss the challenge of scaling from the plot to the globe. Finally, we provide a series of recommendations aimed to improve the connectivity between experiments and LSMs and conclude that studies designed from the perspective of researchers in both communities will provide the greatest benefit to the broader global change community.
Discrimination and Calibration of Clinical Prediction Models: Users’ Guides to the Medical Literature

Accurate information regarding prognosis is fundamental to optimal clinical care. The best approach to assess patient prognosis relies on prediction models that simultaneously consider a number of prognostic factors and provide an estimate of patients’ absolute risk of an event. Such prediction models should be characterized by adequately discriminating between patients who will have an event and those who will not and by adequate calibration ensuring accurate prediction of absolute risk. This Users’ Guide will help clinicians understand the available metrics for assessing discrimination, calibration, and the relative performance of different prediction models. This article complements existing Users’ Guides that address the development and validation of prediction models. Together, these guides will help clinicians to make optimal use of existing prediction models.
Net Loss of CaCO 3 from a subtropical calcifying community due to seawater acidification: mesocosm-scale experimental evidence

Abstract. Acidification of seawater owing to oceanic uptake of atmospheric CO2 originating from human activities such as burning of fossil fuels and land-use changes has raised serious concerns regarding its adverse effects on corals and calcifying communities. Here we demonstrate a net loss of calcium carbonate (CaCO3) material as a result of decreased calcification and increased carbonate dissolution from replicated subtropical coral reef communities (n=3) incubated in continuous-flow mesocosms subject to future seawater conditions. The calcifying community was dominated by the coral Montipora capitata. Daily average community calcification or Net Ecosystem Calcification (NEC=CaCO3 production – dissolution) was positive at 3.3 mmol CaCO3 m−2 h−1 under ambient seawater pCO2 conditions as opposed to negative at −0.04 mmol CaCO3 m−2 h−1 under seawater conditions of double the ambient pCO2. These experimental results provide support for the conclusion that some net calcifying communities could become subject to net dissolution in response to anthropogenic ocean acidification within this century. Nevertheless, individual corals remained healthy, actively calcified (albeit slower than at present rates), and deposited significant amounts of CaCO3 under the prevailing experimental seawater conditions of elevated pCO2.
The 2014 Garrod Lecture: EUCAST - are we heading towards international agreement?

Antimicrobial susceptibility testing with phenotypic methods is based on the measurement of the MIC (mg/L) and breakpoints to categorize bacteria and fungi as susceptible, intermediate or resistant. Phenotypic antimicrobial susceptibility testing requires an agreement on breakpoints and a rigorous standardization of methods and materials. Requirements for defining breakpoints include a definition of doses and dose intervals, information on MIC distributions for the target organisms, definitions of the highest MIC for organisms devoid of phenotypically expressed resistance (the epidemiological cut-off) and information on resistance mechanisms, pharmacokinetics, pharmacodynamics and clinical outcome in trials. In 2001, the breakpoint committees of France, Germany, Norway, Sweden, the Netherlands and the UK were tasked with developing European breakpoints under the umbrella of EUCAST, organized by ESCMID and later also by ECDC. Breakpoints for previously established antibacterial and antifungal agents in Europe have now been harmonized. With the EMA, EUCAST has since 2006 determined breakpoints for new agents. All breakpoints are freely available on the EUCAST web site; these are used in semi-automated antimicrobial susceptibility testing devices and have been employed since 2010 in a EUCAST disc diffusion method. They have been or are now being implemented in most countries inside Europe and many countries outside it. Everything needed to perform and interpret antimicrobial susceptibility testing is freely available from the EUCAST web site, as are aggregated MIC distributions based on more than 26 000 distributions.
Surface Estimation for Microwave Imaging

Biomedical imaging and sensing applications in many scenarios demand accurate surface estimation from a sparse set of noisy measurements. These measurements may arise from a variety of sensing modalities, including laser or electromagnetic samples of an object’s surface. We describe a state-of-the-art microwave imaging prototype that has sensors to acquire both microwave and laser measurements. The approach developed to translate sparse samples of the breast surface into an accurate estimate of the region of interest is detailed. To evaluate the efficacy of the method, laser and electromagnetic samples are acquired by sensors from three realistic breast models with varying sizes and shapes. A set of metrics is developed to assist with the investigation and demonstrate that the algorithm is able to accurately estimate the shape of a realistic breast phantom when only a sparse set of data are available. Moreover, the algorithm is robust to the presence of measurement noise, and is effective when applied to measurement scans of patients acquired with the prototype.
Rigorous benchmarking in reasonable time

Experimental evaluation is key to systems research. Because modern systems are complex and non-deterministic, good experimental methodology demands that researchers account for uncertainty. To obtain valid results, they are expected to run many iterations of benchmarks, invoke virtual machines (VMs) several times, or even rebuild VM or benchmark binaries more than once. All this repetition costs time to complete experiments. Currently, many evaluations give up on sufficient repetition or rigorous statistical methods, or even run benchmarks only in training sizes. The results reported often lack proper variation estimates and, when a small difference between two systems is reported, some are simply unreliable. In contrast, we provide a statistically rigorous methodology for repetition and summarising results that makes efficient use of experimentation time. Time efficiency comes from two key observations. First, a given benchmark on a given platform is typically prone to much less non-determinism than the common worst-case of published corner-case studies. Second, repetition is most needed where most uncertainty arises (whether between builds, between executions or between iterations). We capture experimentation cost with a novel mathematical model, which we use to identify the number of repetitions at each level of an experiment necessary and sufficient to obtain a given level of precision. We present our methodology as a cookbook that guides researchers on the number of repetitions they should run to obtain reliable results. We also show how to present results with an effect size confidence interval. As an example, we show how to use our methodology to conduct throughput experiments with the DaCapo and SPEC CPU benchmarks on three recent platforms.