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AimsQuantile regression is an alternate type of regression analysis that has been shown to have numerous advantages over standard linear regression. Unlike linear regression, which uses the mean to fit a linear model, quantile regression uses a data set's quantiles (or percentiles), which leads to a more comprehensive analysis of the data. However, while relatively common in other scientific fields such as economic and environmental modeling, it is infrequently used to understand biological and microbiological systems.Methods and resultsWe analyzed a set of bacterial growth rates using quantile regression analysis to better understand the effects of antibiotics on bacterial fitness. Using a bacterial model system containing 16 variant genotypes of the TEM β-lactamase enzyme, we compared our quantile regression analysis to a previously published study that uses the Tukey's range test, or Tukey honestly significantly difference (HSD) test. We find that trends in the distribution of bacterial growth rate data, as viewed through the lens of quantile regression, can distinguish between novel genotypes and ones that have been clinically isolated from patients. Quantile regression also identified certain combinations of genotypes and antibiotics that resulted in bacterial populations growing faster as the antibiotic concentration increased-the opposite of what was expected. These analyses can provide new insights into the relationships between enzymatic efficacy and antibiotic concentration.ConclusionsQuantile regression analysis enhances our understanding of the impacts of sublethal antibiotic concentrations on enzymatic (TEM β-lactamase) efficacy and bacterial fitness. We illustrate that quantile regression analysis can link patterns in growth rates with clinically relevant mutations and provides an understanding of how increasing sub-lethal antibiotic concentrations, like those found in our modern environment, can affect bacterial growth rates, and provide insight into the genetic basis for varied resistance.

NON-TECHNICAL SUMMARY:Antibiotic resistance is a global human health problem. We partnered with Dignity Health Mercy Medical Center to study antibiotic resistance in clinical isolates. We tested whether growth rates, a sensitive assay used to measure the fitness of bacterial samples, correlate with a clinical test to measure antibiotic resistance. We found a strong correlation between these two methods suggesting that growth rates could be reliably applied to evolutionary studies of clinically relevant problems. Moreover, the sensitivity of the growth rates assay enabled us to identify fitness effects of specific antibiotic resistance genes.

Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRates and accurately measure variation in growth rate data sets. We define a metric, Variability-score (V-score), that can help determine if variation within a data set might result in false interpretations. CGR also uses the bootstrap method to determine the fraction of bootstrap replicates in which a strain will grow the fastest. We illustrate the usage of CGR with growth rate data sets similar to those in Mira, Meza, et al. (Adaptive landscapes of resistance genes change as antibiotic concentrations change. Mol Biol Evol. 32(10): 2707-2715). These statistical methods are compatible with the analytic methods described in Growth Rates Made Easy and can be used with any set of growth rate output from GrowthRates.

Aim/Purpose: The purpose is a bibliographic review that seeks to offer a clearer vision of the impact of technological tools on the implementation of the flipped classroom model. Background: This document reviews the flipped classroom and secondary education literature to analyze what technological tools are used to implement this model in secondary education settings and their impact on students. Methodology: For the review, an analysis of 61 articles found in SCOPUS and WOS was carried out, processed with a scientometric tool, where the implementation of the inverted classroom model in high school is evident, and, according to these, an in-depth analysis was carried out on which tools are most used and in which contexts they are applied. Contribution: This document offers new researchers a more unobstructed view of what type of technological tools have been implemented in the flipped classroom model in the context of high school and how students perceive them. Findings: This review analyzes the following aspects: (1) Analysis of the leading countries where the flipped classroom model is applied in secondary education; (2) Analysis of the subjects where the flipped classroom model has been implemented; and (3) It offers an analysis of the most used technological tools and in which contexts they are applied (online or offline). Impact on Society: This document helps new researchers make a conscious decision about the type of tool to implement to support the flipped classroom model and shows the importance of developing technological tools that are not limited by the use of the internet so that they can reach a more significant number of people. Future Research: Carry out research that focuses on applications that support low connectivity contexts, in order to improve the implementation of the flipped classroom model.

The development of reliable methods for restoring susceptibility afterantibiotic resistance arises has proven elusive. A greater understanding of therelationship between antibiotic administration and the evolution of resistanceis key to overcoming this challenge. Here we present a data-driven mathematicalapproach for developing antibiotic treatment plans that can reverse theevolution of antibiotic resistance determinants. We have generated adaptivelandscapes for 16 genotypes of the TEM beta-lactamase that vary from the wildtype genotype TEM-1 through all combinations of four amino acid substitutions.We determined the growth rate of each genotype when treated with each of 15beta-lactam antibiotics. By using growth rates as a measure of fitness, wecomputed the probability of each amino acid substitution in each beta-lactamtreatment using two different models named the Correlated Probability Model(CPM) and the Equal Probability Model (EPM). We then performed an exhaustivesearch through the 15 treatments for substitution paths leading from each ofthe 16 genotypes back to the wild type TEM-1. We identified those treatmentpaths that returned the highest probabilities of selecting for reversions ofamino acid substitutions and returning TEM to the wild type state. For the CPMmodel, the optimized probabilities ranged between 0.6 and 1.0. For the EPMmodel, the optimized probabilities ranged between 0.38 and 1.0. For cyclicalCPM treatment plans in which the starting and ending genotype was the wildtype, the probabilities were between 0.62 and 0.7. Overall this study showsthat there is promise for reversing the evolution of resistance throughantibiotic treatment plans.

Introduction: Acute cholecystitis is one of the most common complications of cholelithiasis. In Colombia, there are no studies on its prevalence or the sociodemographic characteristics of the affected population. Objectives: To establish the prevalence of acute cholecystitis in Colombia and describe the associated sociodemographic characteristics. Materials and Methods: A descriptive cross-sectional study was conducted using data from the individual service provision records (RIPS) from 2018 to 2022. Results: A total of 343,254 cases of acute cholecystitis were identified in Colombia, with a prevalence of 681 cases per 100,000 inhabitants and a female-to-male ratio of 2.3:1. The highest number of reported cases was in the 34 to 39-year age group. The departments with the most cases are located in the southeast of the country, including Nariño, Valle del Cauca, Caquetá, and Tolima. Conclusion: This study presents epidemiological and sociodemographic information on acute cholecystitis in Colombia. There are few epidemiological studies on this disease in Latin America, however, the prevalence found is similar to that reported in countries like the United States. [ABSTRACT FROM AUTHOR]

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We are proposing to conduct a multicolor, synoptic infrared (IR) imagingsurvey of the Northern sky with a new, dedicated 6.5-meter telescope at SanPedro M\'artir (SPM) Observatory. This initiative is being developed inpartnership with astronomy institutions in Mexico and the University ofCalifornia. The 4-year, dedicated survey, planned to begin in 2017, will reachmore than 100 times deeper than 2MASS. The Synoptic All-Sky Infrared (SASIR)Survey will reveal the missing sample of faint red dwarf stars in the localsolar neighborhood, and the unprecedented sensitivity over such a wide fieldwill result in the discovery of thousands of z ~ 7 quasars (and reaching to z >10), allowing detailed study (in concert with JWST and Giant Segmented MirrorTelescopes) of the timing and the origin(s) of reionization. As a time-domainsurvey, SASIR will reveal the dynamic infrared universe, opening new phasespace for discovery. Synoptic observations of over 10^6 supernovae and variablestars will provide better distance measures than optical studies alone. SASIRalso provides significant synergy with other major Astro2010 facilities,improving the overall scientific return of community investments. Compared tooptical-only measurements, IR colors vastly improve photometric redshifts to z~ 4, enhancing dark energy and dark matter surveys based on weak lensing andbaryon oscillations. The wide field and ToO capabilities will enable aconnection of the gravitational wave and neutrino universe - with eventsotherwise poorly localized on the sky - to transient electromagnetic phenomena.

We present a novel linear scaling ab initio total energy electronic structure calculation method, which is simple to implement, easily to parallelize, and produces essentially the same results as the direct ab initio method, while it could be thousands of times faster. Using this method, we have studied the dipole moments of CdSe quantum dots, and found both significant bulk and surface contributions. The bulk dipole contribution cannot simply be estimated from the bulk spontaneous polarization value by a proportional volume factor. Instead it has a geometry dependent screening effect. The dipole moment also produces a strong internal electric field which induces a strong electron hole separation.

In this work, we propose a computationally feasible approach todetect severe multiple contingencies. We pose a contingency analysis problem using a nonlinear optimization framework, which enables usto detect the fewest possible transmission line outages resulting ina system failure of specified severity, and the most severe system failure caused by removing a specified number of transmission lines from service. Illustrations using a three bus system and the IEEE ~;30 bus system aim to exhibit the effectiveness of the proposed approach.

Many properties of nanostructures depend on the atomic configuration at the surface. One common technique used for determining this surface structure is based on the low energy electron diffraction (LEED) method, which uses a high-fidelity physics model to compare experimental results with spectra computed via a computer simulation. While this approach is highly effective, the computational cost of the simulations can be prohibitive for large systems. In this work, we propose the use of a direct search method in conjunction with an additive surrogate. This surrogate is constructed from a combination of a simplified physics model and an interpolation that is based on the differences between the simplified physics model and the full fidelity model.

We present a linear scaling 3 dimensional fragment (LS3DF) method that uses a novel decomposition and patching scheme to do ab initio density functional theory (DFT) calculations for large systems. This method cancels out the artificial boundary effects that arise from the spatial decomposition. As a result, the LS3DF results are essentially the same as the original full-system DFT results with errors smaller than the errors introduced by other sources of numerical approximations. In addition, the resulting computational times are thousands of times smaller than conventional DFT methods, making calculations with 100,000 atom systems possible. The LS3DF method is applicable to insulator and semiconductor systems, which covers a current gap in the DOE's materials science code portfolio for large-scale ab initio simulations.

Atomic scale surface structure plays an important role indescribing many properties of materials, especially in the case of nanomaterials. One of the most effective techniques for surface structure determination is low-energy electron diffraction (LEED), which can be used in conjunction with optimization to fit simulated LEED intensities to experimental data. This optimization problem has a number of characteristics that make it challenging: it has many local minima, the optimization variables can be either continuous or categorical, the objective function can be discontinuous, there are no exact analytic derivatives (and no derivatives at all for categorical variables), and function evaluations are expensive. In this study, we show how to apply a particular class of optimization methods known as pattern search methods to address these challenges. These methods donot explicitly use derivatives, and are particularly appropriate when categorical variables are present, an important feature that has not been addressed in previous LEED studies. We have found that pattern search methods can produce excellent results, compared to previously used methods, both in terms of performance and locating optimal results.