Effectiveness of a mitigation measure for the lesser kestrel (Falco naumanni) in wind farms in Spain.

Central-eastern Spain is characterised as being a flat and relatively open landscape, greatly used for agricultural purposes and with a high density of wind installations. This landscape also hosts a large population of the lesser kestrel (Falco naumanni), one of the species most threatened by collisions with wind turbines. During a ten-year period, we analysed bird mortality by recording deaths on three wind farms (WF), Cerro del Palo, Cerro Calderón and La Muela I, located in the province of Cuenca (Spain) and containing a total of 99 turbines. The aim of the study was to determine the variables associated with mortalities caused by these types of devices. Subsequently, the information obtained allowed a mitigation measure to be implemented for avoiding and minimising collisions. The procedure involved superficially tilling the soil around the base of turbines with a high collision rate. This measure was monitored for two years before and after implementation in order to compare its effectiveness, and involved making the areas around the turbines less attractive to kestrels by tilling and reducing the amount of vegetation and consequently the abundance of potential prey, mainly Orthoptera. If effective, the lack of prey would decrease the number of dead kestrels, as the birds of prey would need to search for food in other less dangerous areas (approximately 80 m away from the turbines). After monitoring the mitigation measure it was found that the number of collisions decreased by 75-100%. In fact, no collisions were registered during the two year period for all of the wind turbines with tilled surroundings. Based on these results it can be safely stated that this mitigation measure is an easy and inexpensive procedure that significantly and effectively reduces the number of kestrels that collide into wind turbines.

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.

While COVID-19 vaccines have saved millions of lives, new variants, waxing immunity, unequal rollout and relaxation of mitigation strategies mean that the pandemic will keep on sending shockwaves across healthcare systems. In this context, it is crucial to equip clinicians with tools to triage COVID-19 patients and forecast who will experience the worst forms of the disease. Prediction models based on artificial intelligence could help in this effort, but the task is not straightforward. Indeed, the pandemic is defined by ever-changing factors which artificial intelligence needs to cope with. To be useful in the clinic, a prediction model should make accurate prediction regardless of hospital location, viral variants or vaccination and immunity statuses. It should also be able to adapt its output to the level of resources available in a hospital at any given time. Finally, these tools need to seamlessly integrate into clinical workflows to not burden clinicians. In response, Klén et al. built CODOP, a freely available prediction algorithm that calculates the death risk of patients hospitalized with COVID-19 (https://gomezvarelalab.em.mpg.de/codop/). This model was designed based on biochemical data from routine blood analyses of COVID-19 patients. Crucially, the dataset included 30,000 individuals from 150 hospitals in Spain, the United States, Honduras, Bolivia and Argentina, sampled between March 2020 and February 2022 and carrying most of the main COVID-19 variants (from the original Wuhan version to Omicron). CODOP can predict the death or survival of hospitalized patients with high accuracy up to nine days before the clinical outcome occurs. These forecasting abilities are preserved independently of vaccination status or viral variant. The next step is to tailor the model to the current pandemic situation, which features increasing numbers of infected people as well as accumulating immune protection in the overall population. Further development will refine CODOP so that the algorithm can detect who will need hospitalisation in the next 24 hours, and who will need admission in intensive care in the next two days. Equipping primary care settings and hospitals with these tools will help to restore previous standards of health care during the upcoming waves of infections, particularly in countries with limited resources.

Uncovering the different scales in deer-forest interactions.

Deer are regarded to be a keystone species as they play a crucial role in the way an ecosystem functions. Most deer-forest interaction studies apply a single scale - process of analyzing ecological interactions by only taking into account one dependent variable - to understand how deer browsing behavior shapes different forest components, but they overlook the fact that forests respond to multiple scales simultaneously. This research evaluates the effect of browsing by wild deer on temperate and boreal forests at different scales by synthesizing seminal papers, specifically (a) what are the effects of deer population density in forest regeneration? (b) What are the effects of deer when forests present diverging spatial characteristics? (c) What are the effects on vegetation at different temporal scales? and (d) What are the hierarchical effects of deer when considering other trophic levels? Additionally, a framework based on modern technology is proposed to answer the multiscale research questions previously identified. When analyzing deer-forest interactions at different scales, the strongest relationships occur at the extremes. For example: when deer assemblage occurs in low or high density and is composed of a mix of small and large species. As forests on poor soils remain restrained in size, isolated and chronically browsed. When forests harbor incomplete trophic levels, the effects spill over to lower trophic levels. To better understand the complexities in deer-forest interactions, researchers should combine technology-based instruments like fixed sensors and drones with field-tested methods such observational studies and experiments to tackle multiscale research questions.

Density dependence of daily activity in three ungulate species.

Daily activity in herbivores reflects a balance between finding food and safety. The safety-in-numbers theory predicts that living in higher population densities increases safety, which should affect this balance. High-density populations are thus expected to show a more even distribution of activity-that is, spread-and higher activity levels across the day. We tested these predictions for three ungulate species; red deer (Cervus elaphus), roe deer (Capreolus capreolus), and wild boar (Sus scrofa). We used camera traps to measure the level and spread of activity across ten forest sites at the Veluwe, the Netherlands, that widely range in ungulate density. Food availability and hunting levels were included as covariates. Daily activity was more evenly distributed when population density was higher for all three species. Both deer species showed relatively more feeding activity in broad daylight and wild boar during dusk. Activity level increased with population density only for wild boar. Food availability and hunting showed no correlation with activity patterns. These findings indicate that ungulate activity is to some degree density dependent. However, while these patterns might result from larger populations feeling safer as the safety-in-numbers theory states, we cannot rule out that they are the outcome of greater intraspecific competition for food, forcing animals to forage during suboptimal times of the day. Overall, this study demonstrates that wild ungulates adjust their activity spread and level based on their population size.

Posibles alteraciones de la A.T.M. en cirugías con anestesia general por intubación orotraqueal / Possible alterations of the TMJ in surgery under general anesthesia by endotracheal intubation

En el hospital San Juan de Dios (H.S.J.D.), de Cali, se realizó un estudio descriptivo de la posible relación existente entre la intubación orotraqueal y las alteraciones presentes a nivel de la articulación temporomandibular (A.T.M.). Se evaluaron 174 pacientes del H.S.J.D. intervenidos quirúrgicamente bajo anestesia general con intubación orotraqueal. Para medir el daño articular se utilizó el índice de disfunción de A. T. M. de Hénkimo anamnésico y clínico, pre y post quirúrgicamente. Se encontró, en la comparación de los índices pre y posquirúrgicos, que el 68% de la muestra no tuvo cambio, el 16% aumentó de grado y el otro 16% disminuyó; a pesar de estos resultados se puedo concluir que la afección es mucho más grande de lo que estadísticamente pudiera ser.