Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Monte Carlo study of ion collisions from RHIC to LHC energies

"Attempts to describe nuclear matter under extreme pressure and energy conditions are explored in this work. Different Monte Carlo studies are proposed to study the initial conditions in collisions of ions at relativistic energies where non-perturbative quantum chromodynamics phenomena become relevant. The structure of the medium formed in heavy ion collisions is examined within a model framework. The analysis focuses on the radial distribution function of the transverse representation of model-dependent color flux tubes, aiming to understand the medium’s structure. The findings reveal that the Color String Percolation Model (CSPM) exhibits ideal gas behavior. At the same time, its modified version, known as the core-shell CSPM (CSCSPM), induces a transition from a gas-like to a liquid-like structure. Similarly, the Color Glass Condensate (CGC) framework produces systems that resemble non-ideal gases for AuAu central collisions at RHIC energies and liquid-like structures for PbPb central collisions at LHC energies. On the other hand, the shear and bulk viscosity to entropy density ratios are calculated in the CSPM framework, the effects of system size, which deviate significantly from the thermodynamic limit, are considered"

Colombian surgical outcomes study insights on perioperative mortality rate, a main indicator of the lancet commission on global surgery – a prospective cohort studyResearch in context

Summary: Background: Surgical care holds significant importance in healthcare, especially in low and middle-income countries, as at least 50% of the 4.2 million deaths within the initial 30 days following surgery take place in these countries. The Lancet Commission on Global Surgery proposed six indicators to enhance surgical care. In Colombia, studies have been made using secondary data. However, strategies to reduce perioperative mortality have not been implemented. This study aims to describe the fourth indicator, perioperative mortality rate (POMR), with primary data in Colombia. Methods: A multicentre prospective cohort study was conducted across 54 centres (hospitals) in Colombia. Each centre selected a 7-day recruitment period between 05/2022 and 01/2023. Inclusion criteria involved patients over 18 years of age undergoing surgical procedures in operating rooms. Data quality was ensured through a verification guideline and statistical analysis using mixed-effects multilevel modelling with a case mix analysis of mortality by procedure-related, patient-related, and hospital-related conditions. Findings: 3807 patients were included with a median age of 48 (IQR 32–64), 80.3% were classified as ASA I or II, and 27% of the procedures had a low-surgical complexity. Leading procedures were Orthopedics (19.2%) and Gynaecology/Obstetrics (17.7%). According to the Clavien–Dindo scale, postoperative complications were distributed in major complications (11.7%, 10.68–12.76) and any complication (31.6%, 30.09–33.07). POMR stood at 1.9% (1.48–2.37), with elective and emergency surgery mortalities at 0.7% (0.40–1.23) and 3% (2.3–3.89) respectively. Interpretation: The POMR was higher than the ratio reported in previous national studies, even when patients had a low–risk profile and low-complexity procedures. The present research represents significant public health progress with valuable insights for national decision-makers to improve the quality of surgical care. Funding: This work was supported by Universidad del Rosario and Fundación Cardioinfantil-Instituto de Cardiología grant number CTO-057-2021, project-ID IV-FGV017

International Nosocomial Infection Control Consortiu (INICC) report, data summary of 43 countries for 2007-2012. Device-associated module

We report the results of an International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2007-December 2012 in 503 intensive care units (ICUs) in Latin America, Asia, Africa, and Europe. During the 6-year study using the Centers for Disease Control and Prevention's (CDC) U.S. National Healthcare Safety Network (NHSN) definitions for device-associated health care–associated infection (DA-HAI), we collected prospective data from 605,310 patients hospitalized in the INICC's ICUs for an aggregate of 3,338,396 days. Although device utilization in the INICC's ICUs was similar to that reported from ICUs in the U.S. in the CDC's NHSN, rates of device-associated nosocomial infection were higher in the ICUs of the INICC hospitals: the pooled rate of central line–associated bloodstream infection in the INICC's ICUs, 4.9 per 1,000 central line days, is nearly 5-fold higher than the 0.9 per 1,000 central line days reported from comparable U.S. ICUs. The overall rate of ventilator-associated pneumonia was also higher (16.8 vs 1.1 per 1,000 ventilator days) as was the rate of catheter-associated urinary tract infection (5.5 vs 1.3 per 1,000 catheter days). Frequencies of resistance of Pseudomonas isolates to amikacin (42.8% vs 10%) and imipenem (42.4% vs 26.1%) and Klebsiella pneumoniae isolates to ceftazidime (71.2% vs 28.8%) and imipenem (19.6% vs 12.8%) were also higher in the INICC's ICUs compared with the ICUs of the CDC's NHSN

Measurement of the impact-parameter dependent azimuthal anisotropy in coherent ρ0 photoproduction in Pb–Pb collisions at √sNN = 5.02 TeV

The first measurement of the impact-parameter dependent angular anisotropy in the decay of coherently photoproduced ρ0 mesons is presented. The ρ0 mesons are reconstructed through their decay into a pion pair. The measured anisotropy corresponds to the amplitude of the cos(2ϕ) modulation, where ϕ is the angle between the two vectors formed by the sum and the difference of the transverse momenta of the pions, respectively. The measurement was performed by the ALICE Collaboration at the LHC using data from ultraperipheral Pb−Pb collisions at a center-of-mass energy of sNN−−−√ = 5.02 TeV per nucleon pair. Different impact-parameter regions are selected by classifying the events in nuclear-breakup classes. The amplitude of the cos(2ϕ) modulation is found to increase by about one order of magnitude from large to small impact parameters. Theoretical calculations, which describe the measurement, explain the cos(2ϕ) anisotropy as the result of a quantum interference effect at the femtometer scale that arises from the ambiguity as to which of the nuclei is the source of the photon in the interaction