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Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that finescale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.

While forecasting football match results has long been a popular topic, a practical model for football participants, such as coaches and players, has not been considered in great detail. In this study, we propose a generalized and interpretable machine learning model framework that only requires coaches’ decisions and player quality features for forecasting. By further allowing the model to embed historical match statistics, features that consist of significant information, during the training process the model was practical and achieved both high performance and interpretability. Using five years of data (over 1,700 matches) from the English Premier League, our results show that our model was able to achieve high performance with an F1-score of 0.47, compared to the baseline betting odds prediction, which had an F1-score of 0.39. Moreover, our framework allows football teams to adapt for tactical decision-making, strength and weakness identification, formation and player selection, and transfer target validation. The framework in this study would have proven the feasibility of building a practical match result forecast framework and may serve to inspire future studies.

Background. Volunteer challenge studies have provided detailed data on viral shedding from the respiratory tract before and through the course of experimental influenza virus infection. There are no comparable quantitative data to our knowledge on naturally acquired infections. Methods. In a community-based study in Hong Kong in 2008, we followed up initially healthy individuals to quantify trends in viral shedding on the basis of cultures and reverse-transcription polymerase chain reaction (RT-PCR) through the course of illness associated with seasonal influenza A and B virus infection. Results. Trends in symptom scores more closely matched changes in molecular viral loads measured with RT-PCR for influenza A than for influenza B. For influenza A virus infections, the replicating viral loads determined with cultures decreased to undetectable levels earlier after illness onset than did molecular viral loads. Most viral shedding occurred during the first 2–3 days after illness onset, and we estimated that 1%–8% of infectiousness occurs prior to illness onset. Only 14% of infections with detectable shedding at RT-PCR were asymptomatic, and viral shedding was low in these cases. Conclusions. Our results suggest that “silent spreaders” (ie, individuals who are infectious while asymptomatic or presymptomatic) may be less important in the spread of influenza epidemics than previously thought.

Microbial fuel cells utilize exoelectrogenic microorganisms to directly convert organic matter into electricity, offering a compelling approach for simultaneous power generation and wastewater treatment. However, conventional microbial fuel cells typically require thick biofilms for sufficient metabolic electron production rate, which inevitably compromises mass and charge transport, posing a fundamental tradeoff that limits the achievable power density (<1 mW cm −2 ). Herein, we report a concept for redox-mediated microbial flow fuel cells that utilizes artificial redox mediators in a flowing medium to efficiently transfer metabolic electrons from planktonic bacteria to electrodes. This approach effectively overcomes mass and charge transport limitations, substantially reducing internal resistance. The biofilm-free microbial flow fuel cell thus breaks the inherent tradeoff in dense biofilms, resulting in a maximum current density surpassing 40 mA cm −2 and a highest power density exceeding 10 mW cm −2 , approximately one order of magnitude higher than those of state-of-the-art microbial fuel cells. Exoelectrogenic microorganisms provide a sustainable approach for power generation and wastewater treatment. Here the authors report a redox-mediated microbial flow fuel cell, realizing efficient electron/mass transport and high power output.

Excessive nitrogen runoff leads to degraded water quality, harming human and ecosystem health. We examine the impact of changes in land use and land management for six combinations of socioeconomic pathways and climate outcomes, and find that societal choices will substantially impact riverine total nitrogen loading (+54% to −7%) for the continental United States by the end of the century. Regional impacts will be even larger. Increased loading is possible for both high emission and low emission pathways, due to increased food and biofuel demand, respectively. Some pathways, however, suggest that limiting climate change and eutrophication can be achieved concurrently. Precipitation changes will further exacerbate loading, resulting in a net increase of 1 to 68%. Globally, increases in cropland area and agricultural intensification will likely impact vast portions of Asia. Societal and climate trends must therefore both be considered in designing strategies for managing inland and coastal water quality. Impacts of future changes to land use and land management on eutrophication are not well understood. Here, the authors examine these impacts over the 21st century and find that societal choices will have a huge impact on riverine total nitrogen loading for the continental United States and beyond.

Background. The relationship between seasonal influenza vaccine and susceptibility to 2009 pandemic A/H1N1 virus infection is not fully understood. Methods. One child 6–15 years of age from each of 119 households was randomized to receive 1 dose of inactivated trivalent seasonal influenza vaccine (TIV) or saline placebo in November 2008. Serum samples were collected from study subjects and their household contacts before and 1 month after vaccination (December 2008), after winter (April 2009) and summer influenza (September–October 2009) seasons. Seasonal and pandemic influenza were confirmed by serum hemagglutinination inhibition, viral neutralization titers, and reverse-transcription polymerase chain reaction performed on nasal and throat swab samples collected during illness episodes. Results. TIV recipients had lower rates of serologically confirmed seasonal A/H1N1 infection (TIV group, 8%; placebo group, 21%; P = .10) and A/H3N2 infection (7% vs 12%; P = .49), but higher rates of pandemic A/H1N1 infection (32% vs 17%; P = .09). In multivariable analysis, those infected with seasonal influenza A during the study had a lower risk of laboratory-confirmed pandemic A/H1N1 infection (adjusted odds ratio [OR], 0.35; 95% confidence interval [CI], 0.14–0.87), and receipt of seasonal TIV was unassociated with risk of pandemic A/H1N1 infection (adjusted OR, 1.11; 95% CI, 0.54–2.26). Conclusions. TIV protected against strain-matched infection in children. Seasonal influenza infection appeared to confer cross-protection against pandemic influenza. Whether prior seasonal influenza vaccination affects the risk of infection with the pandemic strain requires additional study. Clinical trials registration. ClinicalTrials.gov number NCT00792051.

Conserved from yeast to humans, Elongator is a protein complex implicated in multiple processes including transcription regulation, α-tubulin acetylation, and tRNA modification, and its defects have been shown to cause human diseases such as familial dysautonomia. Elongator consists of two copies of six core subunits (Elp1, Elp2, Elp3, Elp4, Elp5, and Elp6) that are organized into two subcomplexes: Elp1/2/3 and Elp4/5/6 and form a stable assembly of ~ 850 kDa in size. Although the catalytic subunit of Elongator is Elp3, which contains a radical S -adenosyl- l -methionine (SAM) domain and a putative histone acetyltransferase domain, the Elp4/5/6 subcomplex also possesses ATP-modulated tRNA binding activity. How at the molecular level, Elongator performs its multiple functions and how the different subunits regulate Elongator’s activities remains poorly understood. Here, we provide an overview of the proposed functions of Elongator and describe how recent structural studies provide new insights into the mechanism of action of this multifunctional complex.

Using multigenerational, single-cell tracking we explore the earliest events of biofilm formation by Pseudomonas aeruginosa. During initial stages of surface engagement (≤20 h), the surface cell population of this microbe comprises overwhelmingly cells that attach poorly (∼95% stay <30 s, well below the ∼1-h division time) with little increase in surface population. If we harvest cells previously exposed to a surface and direct them to a virgin surface, we find that these surface-exposed cells and their descendants attach strongly and then rapidly increase the surface cell population. This “adaptive,” time-delayed adhesion requires determinants we showed previously are critical for surface sensing: type IV pili (TFP) and cAMP signaling via the Pil-Chp-TFP system. We show that these surface-adapted cells exhibit damped, coupled out-of-phase oscillations of intracellular cAMP levels and associated TFP activity that persist for multiple generations, whereas surface-naïve cells show uncorrelated cAMP and TFP activity. These correlated cAMP–TFP oscillations, which effectively impart intergenerational memory to cells in a lineage, can be understood in terms of a Turing stochastic model based on the Pil-Chp-TFP framework. Importantly, these cAMP–TFP oscillations create a state characterized by a suppression of TFP motility coordinated across entire lineages and lead to a drastic increase in the number of surface-associated cells with near-zero translational motion. The appearance of this surface-adapted state, which can serve to define the historical classification of “irreversibly attached” cells, correlates with family tree architectures that facilitate exponential increases in surface cell populations necessary for biofilm formation.

The International Union for the Conservation of Nature (IUCN) Red List of ecosystems and Red List of threatened species are global standards for assessing risks of ecosystem collapse and species extinction. However, misconceptions of the Red List assessment process, along with its technically demanding nature, can result in the misapplication of their criteria, leading to inconsistent and potentially unreliable assessments. To address this problem, we developed redlistr, an R package aiding in the production of consistent species and ecosystem Red List assessments. Redlistr's features include methods to calculate 1) area from spatial data, 2) range size metrics, 3) rates of change of distributions or populations, and 4) distribution or population at another time from these rates. A key feature of the package is the systematic approach used to eliminate geometric uncertainty when estimating area of occupancy. Here, we develop two case studies to demonstrate the functionalities of redlistr with typical workflows for both species and ecosystems. Redlistr was developed to be accessible to users with a broad range of experience in programming for spatial and temporal data analysis, and sufficiently flexible to allow users to parameterise functions and select equations to fit their purposes. The package specifically aims to assist researchers and conservation practitioners to conduct robust and transparent risk assessments of ecosystems and species under the IUCN Red List criteria but is also useful for other studies requiring analyses of range size, area change and calculations of rates of change.

To initiate biofilmformation, it is critical for bacteria to sense a surface and respond precisely to activate downstream components of the biofilm program. Type 4 pili (T4P) and increasing levels of c-di-GMP have been shown to be important for surface sensing and biofilm formation, respectively; however, mechanisms important in modulating the levels of this dinucleotide molecule to define a precise output response are unknown. Here, using macroscopic bulk assays and single-cell tracking analyses of Pseudomonas aeruginosa, we uncover a role of the T4P alignment complex protein, PilO, in modulating the activity of the diguanylate cyclase (DGC) SadC. Two-hybrid and bimolecular fluorescence complementation assays, combined with genetic studies, are consistent with a model whereby PilO interacts with SadC and that the PilO–SadC interaction inhibits SadC’s activity, resulting in decreased biofilm formation and increased motility. Using single-cell tracking, we monitor both the mean c-di-GMP and the variance of this dinucleotide in individual cells. Mutations that increase PilO–SadC interaction modestly, but significantly, decrease both the average and variance in c-di-GMP levels on a cell-by-cell basis, while mutants that disrupt PilO–SadC interaction increase the mean and variance of c-di-GMP levels. This work is consistent with a model wherein P. aeruginosa uses a component of the T4P scaffold to fine-tune the levels of this dinucleotide signal during surface commitment. Finally, given our previous findings linking SadC to the flagellar machinery, we propose that this DGC acts as a bridge to integrate T4P and flagellar-derived input signals during initial surface engagement.