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Most antibiotic use is for livestock, and it is growing with the increase in global demand for meat. It is unclear what the increase in demand for antibiotics means for the occurrence of drug resistance in animals and risk to humans. Van Boeckel et al. describe the global burden of antimicrobial resistance in animals on the basis of systematic reviews over the past 20 years (see the Perspective by Moore). There is a clear increase in the number of resistant bacterial strains occurring in chickens and pigs. The current study provides a much-needed baseline model for low- and middle-income countries and provides a “one health” perspective to which future data can be added. Science , this issue p. eaaw1944 ; see also p. 1251 Growing demand for meat in developing economies increases antibiotic consumption and fuels the risk of antibiotic resistance. The global scale-up in demand for animal protein is the most notable dietary trend of our time. Antimicrobial consumption in animals is threefold that of humans and has enabled large-scale animal protein production. The consequences for the development of antimicrobial resistance in animals have received comparatively less attention than in humans. We analyzed 901 point prevalence surveys of pathogens in developing countries to map resistance in animals. China and India represented the largest hotspots of resistance, with new hotspots emerging in Brazil and Kenya. From 2000 to 2018, the proportion of antimicrobials showing resistance above 50% increased from 0.15 to 0.41 in chickens and from 0.13 to 0.34 in pigs. Escalating resistance in animals is anticipated to have important consequences for animal health and, eventually, for human health.

Biopharmaceuticals have established an indisputable presence in the pharmaceutical pipeline, enabling highly specific new therapies. However, manufacturing, isolating, and delivering these highly complex molecules to patients present multiple challenges, including the short shelf-life of biologically derived products. Administration of biopharmaceuticals through inhalation has been gaining attention as an alternative to overcome the burdens associated with intravenous administration. Although most of the inhaled biopharmaceuticals in clinical trials are being administered through nebulization, dry powder inhalers (DPIs) are considered a viable alternative to liquid solutions due to enhanced stability. While freeze drying (FD) and spray drying (SD) are currently seen as the most viable solutions for drying biopharmaceuticals, spray freeze drying (SFD) has recently started gaining attention as an alternative to these technologies as it enables unique powder properties which favor this family of drug products. The present review focus on the application of SFD to produce dry powders of biopharmaceuticals, with special focus on inhalation delivery. Thus, it provides an overview of the critical quality attributes (CQAs) of these dry powders. Then, a detailed explanation of the SFD fundamental principles as well as the different existing variants is presented, together with a discussion regarding the opportunities and challenges of SFD as an enabling technology for inhalation-based biopharmaceuticals. Finally, a review of the main formulation strategies and their impact on the stability and performance of inhalable biopharmaceuticals produced via SDF is performed. Overall, this review presents a comprehensive assessment of the current and future applications of SFD in biopharmaceuticals for inhalation delivery.

Abstract Background Rising antimicrobial resistance (AMR) is a threat to modern medicine, and increasing international mobility facilitates the spread of AMR. Infections with resistant organisms have higher morbidity and mortality, are costlier to treat, result in longer hospital stays and place a greater burden on health systems than infections caused by susceptible organisms. Here we review the role of travel in the international dissemination of AMR and consider actions at the levels of travelers, travel medicine practitioners and policymakers that would mitigate this threat. Results Resistant pathogens do not recognize international borders; travelers to areas with high AMR prevalence are likely to be exposed to resistant bacteria and return to their home countries colonized. Medical tourists go between health facilities with drastically different rates of AMR, potentially transmitting highly resistant strains. Drug-resistant bacteria have been found in every continent; however, differences between countries in the prevalence of AMR depend on multiple factors. These include levels of antibiotic consumption (including inappropriate use), access to clean water, adequate sanitation, vaccination coverage, the availability of quality healthcare and access to high-quality medical products. Conclusions Travelers to areas with high levels of AMR should have vaccines up to date, be aware of ways of treating and preventing travelers’ diarrhea (other than antibiotic use) and be informed on safe sexual practices. The healthcare systems of low- and middle-income countries require investment to reduce the transmission of resistant strains by improving access to clean water, sanitation facilities and vaccines. Efforts are needed to curb inappropriate antibiotic use worldwide. In addition, more surveillance is needed to understand the role of the movement of humans, livestock and food products in resistance transmission. The travel medicine community has a key role to play in advocating for the recognition of AMR as a priority on the international health agenda. Key policy recommendations AMR is a threat to modern medicine, and international travel plays a key role in the spread of highly resistant strains. It is essential that this is addressed at multiple levels. Individual travelers can reduce antibiotic consumption and the likelihood of infection. Travelers should have up-to-date vaccines and be informed on methods of preventing and treating travelers’ diarrhea, other than use of antibiotics and on safe sexual practices, such as condom use. Healthcare facilities need to be aware of the travel history of patients to provide appropriate treatment to those who are at high risk of exposure and to prevent further spread. Internationally, in countries without reliable and universal access to clean water, sanitation and hygiene, investment is needed to reduce the emergence and spread of resistance and ensure the antimicrobials available are of assured quality. High-income countries must ensure their use of antimicrobials is appropriate to reduce selection for AMR. Surveillance across all countries is needed to monitor and respond to this emerging threat.

A bstract Jet production at hadron colliders is a benchmark process to probe the dynamics of the strong interaction and the structure of the colliding hadrons. One of the most basic jet production observables is the single jet inclusive cross section, which is obtained by summing all jets that are observed in an event. Our recent computation of next-to-next-to-leading order (NNLO) QCD contributions to single jet inclusive observables uncovered large corrections in certain kinematical regions, which also resulted in a sizeable ambiguity on the appropriate choice of renormalization and factorization scales. We now perform a detailed investigation of the infrared sensitivity of the different ingredients to the single jet inclusive cross section. We show that the contribution from the second jet, ordered in transverse momentum p T , in the event is particularly sensitive to higher order effects due to implicit restrictions on its kinematics. By investigating the second-jet transverse momentum distribution, we identify large-scale cancellations between different kinematical event configurations, which are aggravated by certain types of scale choice. Taking perturbative convergence and stability as selection criteria enables us to single out the total partonic transverse energy Ĥ T and twice the individual jet transverse momentum 2 p T (with which Ĥ T coincides in Born kinematics) as the most appropriate scales in the perturbative description of single jet inclusive production.

Traditional food packaging systems help reduce food wastage, but they also produce environmental impacts when not properly disposed of. Bio-based polymers are a promising solution to overcome these impacts, but they have poor barrier and mechanical properties. This work evaluates two strategies to improve these properties in pectin films: the incorporation of cellulose nanocrystals (CNC) or sodium montmorillonite (MMT) nanoparticles, and an additional layer of chitosan (i.e., a bilayer film). The bionanocomposites and bilayer films were characterized in terms of optical, morphological, hygroscopic, mechanical and barrier properties. The inclusion of the nanofillers in the polymer reduced the water vapor permeability and the hydrophilicity of the films without compromising their visual properties (i.e., their transparency). However, the nanoparticles did not substantially improve the mechanical properties of the bionanocomposites. Regarding the bilayer films, FTIR and contact angle studies revealed no surface and/or chemical modifications, confirming only physical coating/lamination between the two polymers. These bilayer films exhibited a dense homogenous structure, with intermediate optical and hygroscopic properties. An additional layer of chitosan did not improve the mechanical, water vapor and oxygen barrier properties of the pectin films. However, this additional layer made the material more hydrophobic, which may play an important role in the application of pectin as a food packaging material.

The importance of accurately predicting student performance in education, especially in the challenging curricular unit of Introductory Programming, cannot be overstated. As institutions struggle with high failure rates and look for solutions to improve the learning experience, the need for effective prediction methods becomes critical. This study aims to conduct a systematic review of the literature on methods for predicting student performance in higher education, specifically in Introductory Programming, focusing on machine learning algorithms. Through this study, we not only present different applicable algorithms but also evaluate their performance, using identified metrics and considering the applicability in the educational context, specifically in higher education and in Introductory Programming. The results obtained through this study allowed us to identify trends in the literature, such as which machine learning algorithms were most applied in the context of predicting students’ performance in Introductory Programming in higher education, as well as which evaluation metrics and datasets are usually used.

The development of innovative/sustainable materials capable of enlarging the shelf-life of food products has lately been a focus of research, aiming to reduce food waste. Due to their good antimicrobial properties, zinc oxide nanoparticles (ZnO NPs) can add activity to food packaging, improving its performance. Furthermore, these nanoparticles are considered GRAS by the Food and Drug Administration (FDA), which represents an advantage in their application. Through an innovative and sustainable approach using tomato and passionfruit extracts, ZnO NPs were produced and incorporated into pectin films. The resulting bionanocomposites were tested for their activity via in situ studies, using fresh poultry meat as a food matrix. Overall, the bionanocomposites presented good antimicrobial activity, with the intrinsic antimicrobial properties of pectin having shown to be enhanced by the incorporated ZnO NPs. When used as primary packaging for the meat, the deterioration rate of the poultry meat, measured through microbiological growth and total volatile basic nitrogen content, was reduced. However, the nanoparticles contributed to the increment of discoloration and meat oxidation processes. Nonetheless, it can be concluded that fresh poultry meat protected with the bionanocomposites presented an extension of its shelf-life time, and it was confirmed that this eco-friendly packaging has potential to be employed by the food industry.

Core Ideas Soybean yield response to seeding rate was dependent on yield environment. Optimum seeding rate increased as yield environments were reduced. Seeding rate could be reduced by 18% for high‐yielding relative to low‐yielding environments, without penalizing yields. Planting date interacts with seed yield response to seeding rate, optimum seeding rates increase with late planting. For high‐yielding environment, late planting time decreased yields regardless of the seeding rate. Optimizing seed inputs while increasing farming profit is the main purpose of variable rate seeding (VRS) technology adoption. Previous studies in corn (Zea mays L.) suggested that optimal seeding rates increase as yield productivity level increased. For soybean [Glycine max (L.) Merr.], optimal yield‐to‐seeding rate by yield level has not been fully investigated, representing a scientific knowledge gap. Therefore, a dataset was collected from 109 replicated field trials from Southern Brazil (2180 experimental units) presenting the following objectives: (i) identify the optimum seeding rate at varying yield levels (herein termed as yield environments), and (ii) explore the contribution of management factors (i.e., seeding rate, planting date, row spacing, maturity groups, growing season, yield environment, and ecological region) on soybean seed yield. Hierarchical modeling and Bayesian statistical inference were used to predict optimum seeding rate at varying yield environments, while conditional inference tree analysis was explored to identify and rank factors contributing to yield variation. The main results were: (i) soybean seeding rate increased from high‐ to low‐yielding environments; (ii) seeding rate could be reduced by 18% in high‐yielding (>5 Mg ha−1) relative to the low‐yielding (<4 Mg ha−1) environments, without penalizing yields. For improving site‐specific soybean seeding rate prescriptions, future studies should focus on the physiological mechanisms underpinning yield formation and on understanding the main factors (soil × plant × weather) contributing to the differential optimum seeding rate response.

PurposeTo explore the interplay between human translators and AI tools, focusing on tertiary students' perceptions in the context of Portuguese-Chinese translations in Macao.Design/methodology/approachThis research employed a mixed-methods approach. Quantitative surveys were complemented by qualitative responses. Qualitative class observations (participant and non-participant) and autoethnography further enriched the insights. Participants included undergraduate and postgraduate students in translation studies from the Macao Polytechnic University.FindingsThe data revealed a dual perspective: appreciation for AI’s efficiency contrasted with concerns about its potential to overshadow human touch in translations, especially in cultural nuances. Views on integrating AI into curricula were diverse, but a balanced, synergistic approach between human expertise and AI efficiency emerged as a common theme.Originality/valueThis study offers a fresh perspective by integrating various methodologies, capturing both statistical and experiential insights on the evolving relationship between AI and human translation efforts in academia.

The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. In this line of action, the Portuguese Institute of Nature and Forest Conservation defined a fire break network (FBN), which helps controlling wildfires. However, these fire breaks are efficient only if they are correctly maintained, which should be ensured by the local authorities and requires verification from the national authorities. This is a fastidious task since they have a large network of thousands of hectares to monitor over a full year. With the increasing quality and frequency of the Earth Observation Satellite imagery with Sentinel-2 and the definition of the FBN, a semi-automatic remote sensing methodology is proposed in this article for the detection of maintenance operations in a fire break. The proposed methodology is based on a time-series analysis, an object-based classification and a change detection process. The change detection is ensured by an artificial neural network, with reflectance bands and spectral indices as features. Additionally, an analysis of several bands and spectral indices is presented to show the behaviour of the data during a full year and in the presence of a maintenance operation. The proposed methodology achieved a relative error lower than 4% and a recall higher than 75% on the detection of maintenance operations.