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• The plant area index (PAI) is a structural trait that succinctly parametrizes the foliage distribution of a canopy and is usually estimated using indirect optical techniques such as digital hemispherical photography. Critically, on-the-ground photographic measurements forgo the vertical variation of canopy structure which regulates the local light environment. Hence new approaches are sought for vertical sampling of traits. • We present an uncrewed aircraft system (UAS) spherical photographic method to obtain structural traits throughout the depth of tree canopies. Our method explained 89% of the variation in PAI when compared with ground-based hemispherical photography. • When comparing UAS vertical trait profiles with airborne laser scanning data, we found highest agreement in an open birch (Betula pendula/pubescens) canopy. Minor disagreement was found in dense spruce (Picea abies) stands, especially in the lower canopy. • Our new method enables easy estimation of the vertical dimension of canopy structural traits in previously inaccessible spaces. The method is affordable and safe and therefore readily usable by plant scientists.

The search for new biomarkers that allow an early diagnosis in sepsis and predict its evolution has become a necessity in medicine. The objective of this study is to identify, through omics techniques, potential protein biomarkers that are expressed in patients with sepsis and their relationship with organ dysfunction and mortality. Prospective, observational and single-center study that included adult patients (≥ 18 years) who were admitted to a tertiary hospital and who met the criteria for sepsis. A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. Subsequently, using recursive feature elimination classification and cross-validation with a vector classifier, an association of these proteins with mortality and organ dysfunction was established. The protein-protein interaction network was analyzed with String software. 141 patients were enrolled in this study. Mass spectrometry identified 177 proteins. Of all of them, and by recursive feature elimination, nine proteins (GPX3, APOB, ORM1, SERPINF1, LYZ, C8A, CD14, APOC3 and C1QC) were associated with organ dysfunction (SOFA > 6) with an accuracy of 0.82 ± 0.06, precision of 0.85 ± 0.093, sensitivity 0.81 ± 0.10, specificity 0.84 ± 0.10 and AUC 0.82 ± 0.06. Twenty-two proteins (CLU, LUM, APOL1, SAA1, CLEBC3B, C8A, ITIH4, KNG1, AGT, C7, SAA2, APOH, HRG, AFM, APOE, APOC1, C1S, SERPINC1, IGFALS, KLKB1, CFB and BTD) were associated with mortality with an accuracy of 0.86 ± 0.05, a precision of 0.91 ± 0.05, a sensitivity of 0.91 ± 0.05, a specificity of 0.72 ± 0.17, and an area under the curve (AUC) of 0.81 ± 0.08 with a confidence interval of 95%. In sepsis there are proteomic patterns associated with organ dysfunction and mortality.

Accurate and automated analysis of chest Computed Tomography (CT) scans is critical for early detection and risk stratification of lung cancer, the leading cause of cancer-related mortality worldwide. However, the development of robust deep learning models for lung nodule analysis is hindered by the limited availability of large, diverse, and well-annotated 3D CT datasets. This work presents an anatomically guided latent diffusion framework for synthesizing high-quality three-dimensional chest CT volumes. The proposed approach, termed LAND (Lung and Nodule Diffusion), conditions the generative process on 3D anatomical masks of the lungs and pulmonary nodules to ensure accurate spatial localization and realistic anatomical structure. A dedicated variational autoencoder (VAE) encodes anatomical masks into a latent representation that preserves fine-grained nodule morphology. In addition, conditional texture modeling within masked nodule regions enables controlled variation in lesion appearance. Compared with existing 3D diffusion-based methods, LAND substantially reduces computational requirements and generates 256 × 256 × 256 volumes at 1 mm isotropic resolution using 10–16 GB of GPU memory during training and less than 8 GB during inference. Experimental results demonstrate high visual fidelity and anatomical realism, further supported by improved performance in downstream lung nodule segmentation and classification tasks. These findings indicate that LAND provides a practical and efficient framework for anatomically guided 3D medical image synthesis and data augmentation.

Mediterranean forests of Aleppo pine (Pinus halepensis Mill.) have a crucial role in climate change, as they are extremely adaptive and provide valuable timber or carbon stocks. However, greater detail quantifying those attributes is needed: although National Forest Inventories are acceptable, continuous cover maps are normally lacking. Here, we use the public Spanish low-density LiDAR flights to model above-ground biomass, volume, tree density, basal area and dominant height of naturally regenerated Mediterranean Aleppo pine forests, comparing individual-tree detection and area-based approach. We found R2 and RRMSE among 0.51–0.66 and 40–34% for above-ground biomass, 0.54–0.70 and 34–28% for volume, 0.23–0.45 and 33–28% for tree density, 0.48–0.62 and 32–27% for basal area, and 0.70–0.69 and 11–11% for dominant height. In all cases but dominant height, the area-based approach outperformed the individual-tree detection. Neither time difference between LiDAR flight and ground measurement or past land use affected the area-based approach models, yet the latter had a strong effect on observed productivity. The different definitions of dominant height were equivalent and did not influence the dominant height models. We believe these models, and their corresponding maps, will be a great asset for policymakers and different stakeholders for Aleppo pine forests throughout the Mediterranean basin.

Background Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments. Results The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU. Conclusion We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.

The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. This study aims to identify protein biomarkers that differentiate sepsis from non-infectious systemic inflammatory response syndrome (NISIRS), addressing the need for early sepsis diagnosis. Prospective observational study of a cohort of septic patients activated by the Sepsis Code and patients admitted with NISIRS, during the period 2016-2018. A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. Subsequently, using recursive feature elimination (RFE) classification and cross-validation with logistic regression, an association of these proteins in patients with sepsis compared to patients with NISIRS. The protein-protein interaction network was analyzed with String software. 275 patients were included (139 with sepsis and 136 with NISIRS. Plasma proteins were analyzed using mass spectrometry and evaluated through recursive feature elimination and cross-validation with a vector classifier. Twenty-five proteins showed statistically significant differences, with high diagnostic performance (sensitivity: 0.973, specificity: 0.920, accuracy: 0.960, AUC: 0.985). Fourteen proteins (VWF, PPBP, C5, C1RL, FCN3, SAA2, ORM1, ITIH3, GSN, C1QA, CA1, CFB, C3, LBP) were more associated with sepsis, while eleven (FN1, IGFALS, SERPINA4, APOE, APOH, C6, SERPINA3, AHSG, LUM, ITIH2, SAA1) were linked to NISIRS. The study found upregulation of several proteins in sepsis (C5, CFB, FCN3, PPBP, VWF, SAA2, ORM1, LBP) and downregulation of others (ITIH3, SERPINA4, AHSG). These findings highlight distinct proteomic patterns between sepsis and NISIRS. Advances in understanding these protein changes may allow for the identification of new biomarkers in the future.

ERα is expressed in macrophages and other immune cells known to exert dramatic effects on glucose homeostasis. We investigated the impact of ERα expression on macrophage function to determine whether hematopoietic or myeloid-specific ERα deletion manifests obesity-induced insulin resistance in mice. Indeed, altered plasma adipokine and cytokine levels, glucose intolerance, insulin resistance, and increased adipose tissue mass were observed in animals harboring a hematopoietic or myeloid-specific deletion of ERα. A similar obese phenotype and increased atherosclerotic lesion area was displayed in LDL receptor-KO mice transplanted with ERα–/– bone marrow. In isolated macrophages, ERα was necessary for repression of inflammation, maintenance of oxidative metabolism, IL-4–mediated induction of alternative activation, full phagocytic capacity in response to LPS, and oxidized LDL-induced expression of ApoE and Abca1. Furthermore, we identified ERα as a direct regulator of macrophage transglutaminase 2 expression, a multifunctional atheroprotective enzyme. Our findings suggest that diminished ERα expression in hematopoietic/myeloid cells promotes aspects of the metabolic syndrome and accelerates atherosclerosis in female mice.

Circulatory shock is a life-threatening disease that accounts for around one-third of all admissions to intensive care units (ICU). It requires immediate treatment, which is why the development of tools for planning therapeutic interventions is required to deal with shock in the critical care environment. In this study, the ShockOmics European project original database is used to extract attributes capable of predicting mortality due to shock in the ICU. Missing data imputation techniques and machine learning models were used, followed by feature selection from different data subsets. Selected features were later used to build Bayesian Networks, revealing causal relationships between features and ICU outcome. The main result is a subset of predictive features that includes well-known indicators such as the SOFA and APACHE II scores, but also less commonly considered ones related to cardiovascular function assessed through echocardiograpy or shock treatment with pressors. Importantly, certain selected features are shown to be most predictive at certain time-steps. This means that, as shock progresses, different attributes could be prioritized. Clinical traits obtained at 24h. from ICU admission are shown to accurately predict cardiogenic and septic shock mortality, suggesting that relevant life-saving decisions could be made shortly after ICU admission.

Introduction The search for new biomarkers that allow an early diagnosis in sepsis and predict its evolution has become a necessity in medicine. The objective of this study is to identify, through omics techniques, potential protein biomarkers that are expressed in patients with sepsis and their relationship with organ dysfunction and mortality. Methods Prospective, observational and single-center study that included adult patients (≥ 18 years) who were admitted to a tertiary hospital and who met the criteria for sepsis. A mass spectrometry-based approach was used to analyze the plasma proteins in the enrolled subjects. Subsequently, using recursive feature elimination classification and cross-validation with a vector classifier, an association of these proteins with mortality and organ dysfunction was established. The protein-protein interaction network was analyzed with String software. Results 141 patients were enrolled in this study. Mass spectrometry identified 177 proteins. Of all of them, and by recursive feature elimination, nine proteins (GPX3, APOB, ORM1, SERPINF1, LYZ, C8A, CD14, APOC3 and C1QC) were associated with organ dysfunction (SOFA > 6) with an accuracy of 0.82 ± 0.06, precision of 0.85 ± 0.093, sensitivity 0.81 ± 0.10, specificity 0.84 ± 0.10 and AUC 0.82 ± 0.06. Twenty-two proteins (CLU, LUM, APOL1, SAA1, CLEBC3B, C8A, ITIH4, KNG1, AGT, C7, SAA2, APOH, HRG, AFM, APOE, APOC1, C1S, SERPINC1, IGFALS, KLKB1, CFB and BTD) were associated with mortality with an accuracy of 0.86 ± 0.05, a precision of 0.91 ± 0.05, a sensitivity of 0.91 ± 0.05, a specificity of 0.72 ± 0.17, and an area under the curve (AUC) of 0.81 ± 0.08 with a confidence interval of 95%. Conclusion In sepsis there are proteomic patterns associated with organ dysfunction and mortality.

A precise balance between protein degradation and synthesis is essential to preserve skeletal muscle mass. Here, we found that TP53INP2, a homolog of the Drosophila melanogaster DOR protein that regulates autophagy in cellular models, has a direct impact on skeletal muscle mass in vivo. Using different transgenic mouse models, we demonstrated that muscle-specific overexpression of Tp53inp2 reduced muscle mass, while deletion of Tp53inp2 resulted in muscle hypertrophy. TP53INP2 activated basal autophagy in skeletal muscle and sustained p62-independent autophagic degradation of ubiquitinated proteins. Animals with muscle-specific overexpression of Tp53inp2 exhibited enhanced muscle wasting in streptozotocin-induced diabetes that was dependent on autophagy; however, TP53INP2 ablation mitigated experimental diabetes-associated muscle loss. The overexpression or absence of TP53INP2 did not affect muscle wasting in response to denervation, a condition in which autophagy is blocked, further indicating that TP53INP2 alters muscle mass by activating autophagy. Moreover, TP53INP2 expression was markedly repressed in muscle from patients with type 2 diabetes and in murine models of diabetes. Our results indicate that TP53INP2 negatively regulates skeletal muscle mass through activation of autophagy. Furthermore, we propose that TP53INP2 repression is part of an adaptive mechanism aimed at preserving muscle mass under conditions in which insulin action is deficient.