Miracle-dz/UniFAR-FTUs huggingface dataset:
Canopy gaps and associated losses of biomass – combining UAV imagery and field data in a central Amazon forest

Abstract. Understanding mechanisms of tree mortality and the dynamics of associated canopy gaps is relevant for robust estimates of carbon balance in forests. We combined monthly RGB images acquired from an unoccupied aerial vehicle with field surveys to identify gaps in an 18 ha plot installed in an old-growth central Amazon forest. We measured the size and shape of gaps and analyzed their temporal variation and correlation with rainfall over a period of 28 months. We further described associated modes of tree mortality (i.e., snapping, uprooting and standing dead) and branch fall and quantified associated losses of biomass. In total, we detected 32 gaps either in the images or field ranging in area from 9 to 835 m2. Relatively small gaps (< 39 m2) opened by branch fall were the most frequent (11 gaps). Out of 18 gaps for which both field and image data were available, three could not be detected remotely. Gaps observed in the field but not captured on the imagery were relatively small and mainly formed by the fall of branches from live and standing dead trees. Our data show that ∼ 17 % of the tree-mortality and branch-fall events only affected the lower canopy and the understory of the forest and are likely neglected by top-of-the-canopy assessments. Regardless of the detection method, the size distribution was best described by a lognormal function for gaps starting from the smallest detected size (9 and 10 m2 for field and imagery data, respectively), and the Weibull and Power functions for gaps larger than 25 m2. Properly assessing associated confidence intervals requires larger sample sizes. Repeated field measurements reveal that gap area does not differ significantly among modes of tree mortality or branch fall in central Amazon forests, with the last contributing the least to biomass loss. Predicting mechanisms of gap formation based on associated area and biomass loss remains challenging, which highlights the need for larger datasets. The rate of gap area formation was positively correlated with the frequency of extreme rainfall events, which may be related to a higher frequency of storms propagating extreme rain and wind gusts. While remote sensing has proven to be an accurate and precise method for mapping gaps compared to field data (i.e., ground truth), it is important to note that our sample size was relatively small. Therefore, the extrapolation of these results beyond our study region and landscape shall be made cautiously. Apart from improving landscape assessments of carbon balance, regional information on gap dynamics and associated mechanisms of formation are fundamental to address forest responses to altered disturbance regimes resulting from climate change.
Salt-Intake-Related Behavior Varies between Sexes and Is Strongly Associated with Daily Salt Consumption in Obese Patients at High Risk for MASLD

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) imposes a significant burden on Westernized regions. The Western diet, high in salt intake, significantly contributes to disease development. However, there are a lack of data on salt literacy and salt intake among MASLD patients in Germany. Our study aims to analyze daily salt intake and salt-intake-related behavior in MASLD patients. Methods: 234 MASLD patients were prospectively included. Daily salt intake and salt-intake-related behavior were assessed via a food frequency questionnaire (FFQ—DEGS) and a salt questionnaire (SINU). Statistical analyses were performed using SPSS. Results: Mean daily salt intake was higher in men than in women (7.3 ± 5 g/d vs. 5.3 ± 4 g/d; p < 0.001). There was significant agreement between increased daily salt intake (>6 g/d) and the behavioral salt index (SI) (p < 0.001). Men exhibited higher SI scores compared to women, indicating lower awareness of salt in everyday life. Multivariate analysis identified specific salt-intake-related behaviors impacting daily salt consumption. Conclusions: Our study reveals a strong link between daily salt intake and salt-intake-related behavior, highlighting sex-specific differences in an MASLD cohort. To enhance patient care in high-cardiovascular-risk populations, specific behavioral approaches may be considered, including salt awareness, to improve adherence to lifestyle changes, particularly in male patients.
Circulating Sphingolipids in Insulin Resistance, Diabetes and Associated Complications

Sphingolipids play an important role in the development of diabetes, both type 1 and type 2 diabetes, as well as in the development of both micro- and macro-vascular complications. Several reviews have been published concerning the role of sphingolipids in diabetes but most of the emphasis has been on the possible mechanisms by which sphingolipids, mainly ceramides, contribute to the development of diabetes. Research on circulating levels of the different classes of sphingolipids in serum and in lipoproteins and their importance as biomarkers to predict not only the development of diabetes but also of its complications has only recently emerged and it is still in its infancy. This review summarizes the previously published literature concerning sphingolipid-mediated mechanisms involved in the development of diabetes and its complications, focusing on how circulating plasma sphingolipid levels and the relative content carried by the different lipoproteins may impact their role as possible biomarkers both in the development of diabetes and mainly in the development of diabetic complications. Further studies in this field may open new therapeutic avenues to prevent or arrest/reduce both the development of diabetes and progression of its complications.
Perspectives of targeting LILRB1 in innate and adaptive immune checkpoint therapy of cancer

Immune checkpoint blockade is a compelling approach in tumor immunotherapy. Blocking inhibitory pathways in T cells has demonstrated clinical efficacy in different types of cancer and may hold potential to also stimulate innate immune responses. A novel emerging potential target for immune checkpoint therapy is leukocyte immunoglobulin-like receptor subfamily B member 1 (LILRB1). LILRB1 belongs to the superfamily of leukocyte immunoglobulin-like receptors and exerts inhibitory functions. The receptor is expressed by a variety of immune cells including macrophages as well as certain cytotoxic lymphocytes and contributes to the regulation of different immune responses by interaction with classical as well as non-classical human leukocyte antigen (HLA) class I molecules. LILRB1 has gained increasing attention as it has been demonstrated to function as a phagocytosis checkpoint on macrophages by recognizing HLA class I, which represents a ‘Don’t Eat Me! ’ signal that impairs phagocytic uptake of cancer cells, similar to CD47. The specific blockade of the HLA class I:LILRB1 axis may provide an option to promote phagocytosis by macrophages and also to enhance cytotoxic functions of T cells and natural killer (NK) cells. Currently, LILRB1 specific antibodies are in different stages of pre-clinical and clinical development. In this review, we introduce LILRB1 and highlight the features that make this immune checkpoint a promising target for cancer immunotherapy.
Resolving complex anaphors Evidence from online comprehension

This study investigates the question of whether the processing of complex anaphors require more cognitive effort than the processing of NP-anaphors. Complex anaphors refer to abstract objects which are not introduced as a noun phrase and bring about the creation of a new discourse referent. This creation is called “complexation process” (see also Consten et al. this volume). We describe ERP findings which provide converging support for the assumption that the cognitive cost of this complexation process is higher than the cognitive cost of processing NP-anaphors.
Multilevel Self-Training Approach for Cross-Domain Semantic Segmentation in Intelligent Vehicles

The use of intelligent vehicle technology is increasing; however, this technology requires further improvement. Semantic segmentation enables intelligent vehicles to understand the environment. Although advances have been achieved in a deep learning model for semantic segmentation, large-scale real-world datasets with manual pixel-level annotations, which are expensive, are required for adequately training these models. Unsupervised domain adaptation (UDA) for semantic segmentation enables training on a synthetic source domain and allows a model to be adapted to the real-world target domain without target domain annotations. Self-training is a highly effective UDA method; however, current UDA methods struggle to shift domains effectively and rely heavily on aggressive image-based perturbations for consistency training, resulting in poor-quality pseudolabels during the early training phase and decreased training efficiency. Therefore, we propose a novel multilevel self-training UDA method for semantic segmentation. First, a feature guidance and resampling strategy is adopted to improve the learning of rare classes in the source domain, and a spatial-aware mixing method is then used for gradually and smoothly eliminating the domain gap through training in various cross-domain intermediate spaces. In addition, we developed an instance and prototype feature consistency technique that explores optimization in the feature space to improve the training process by increasing model consistency. We conducted large-scale experiments on two challenging UDA benchmarks, “GTA5-to-Cityscapes” and “SYNTHIA-to-Cityscapes. ” Our method achieved similar or superior performance to that of previously developed UDA methods for semantic segmentation and has advantages in terms of training stability and convergence efficiency.
The Comprehensive Analysis of m6A-Associated Anoikis Genes in Low-Grade Gliomas

The relationship between N6-methyladenosine (m6A) regulators and anoikis and their effects on low-grade glioma (LGG) is not clear yet. The TCGA-LGG cohort, mRNAseq 325 dataset, and GSE16011 validation set were separately obtained via the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Altas (CGGA), and Gene Expression Omnibus (GEO) databases. In total, 27 m6A-related genes (m6A-RGs) and 508 anoikis-related genes (ANRGs) were extracted from published articles individually. First, differentially expressed genes (DEGs) between LGG and normal samples were sifted out by differential expression analysis. DEGs were respectively intersected with m6A-RGs and ANRGs to acquire differentially expressed m6A-RGs (DE-m6A-RGs) and differentially expressed ANRGs (DE-ANRGs). A correlation analysis of DE-m6A-RGs and DE-ANRGs was performed to obtain DE-m6A-ANRGs. Next, univariate Cox and least absolute shrinkage and selection operator (LASSO) were performed on DE-m6A-ANRGs to sift out risk model genes, and a risk score was gained according to them. Then, gene set enrichment analysis (GSEA) was implemented based on risk model genes. After that, we constructed an independent prognostic model and performed immune infiltration analysis and drug sensitivity analysis. Finally, an mRNA-miRNA-lncRNA regulatory network was constructed. There were 6901 DEGs between LGG and normal samples. Six DE-m6A-RGs and 214 DE-ANRGs were gained through intersecting DEGs with m6A-RGs and ANRGs, respectively. A total of 149 DE-m6A-ANRGs were derived after correlation analysis. Four genes, namely ANXA5, KIF18A, BRCA1, and HOXA10, composed the risk model, and they were involved in apoptosis, fatty acid metabolism, and glycolysis. The age and risk scores were finally sifted out to construct an independent prognostic model. Activated CD4 T cells, gamma delta T cells, and natural killer T cells had the largest positive correlations with risk model genes, while activated B cells were significantly negatively correlated with KIF18A and BRCA1. AT.9283, EXEL.2280, Gilteritinib, and Pracinostat had the largest correlation (absolute value) with a risk score. Four risk model genes (mRNAs), 12 miRNAs, and 21 lncRNAs formed an mRNA-miRNA-lncRNA network, containing HOXA10-hsa-miR-129-5p-LINC00689 and KIF18A-hsa-miR-221-3p-DANCR. Through bioinformatics, we constructed a prognostic model of m6A-associated anoikis genes in LGG, providing new ideas for research related to the prognosis and treatment of LGG.
Can Pharmaceutical Excipients Threaten the Aquatic Environment? A Risk Assessment Based on the Microtox® Biotest

The ecotoxicological impact of pharmaceuticals has received considerable attention, primarily focusing on active pharmaceutical ingredients (APIs) while largely neglecting the potential hazards posed by pharmaceutical excipients. Therefore, we analyzed the ecotoxicity of 16 commonly used pharmaceutical excipients, as well as 26 API–excipient and excipient–excipient mixtures utilizing the Microtox® test. In this way, we assessed the potential risks that pharmaceutical excipients, generally considered safe, might pose to the aquatic environment. We investigated both their individual ecotoxicity and their interactions with tablet ingredients using concentration addition (CA) and independent action (IA) models to shed light on the often-overlooked ecotoxicological consequences of these substances. The CA model gave a more accurate prediction of toxicity and should be recommended for modeling the toxicity of combinations of drugs with different effects. A challenge when studying the ecotoxicological impact of some pharmaceutical excipients is their poor water solubility, which hinders the use of standard aquatic ecotoxicity testing techniques. Therefore, we used a modification of the Microtox® Basic Solid Phase protocol developed for poorly soluble substances. The results obtained suggest the high toxicity of some excipients, i.e., SLS and meglumine, and confirm the occurrence of interactions between APIs and excipients. Through this research, we hope to foster a better understanding of the ecological impact of pharmaceutical excipients, prompting the development of risk assessment strategies within the pharmaceutical industry.
Complement inhibition can decrease the haemostatic response in a microvascular bleeding model at multiple levels

Background Haemostasis is a crucial process by which the body stops bleeding. It is achieved by the formation of a platelet plug, which is strengthened by formation of a fibrin mesh mediated by the coagulation cascade. In proinflammatory and prothrombotic conditions, multiple interactions of the complement system and the coagulation cascade are known to aggravate thromboinflammatory processes and increase the risk of arterial and venous thrombosis. Whether those interactions also play a relevant role during the physiological process of haemostasis is not yet completely understood. The aim of this study was to investigate the potential role of complement components and activation during the haemostatic response to mechanical vessel injury. Methods We used a microvascular bleeding model that simulates a blood vessel, featuring human endothelial cells, perfusion with fresh human whole blood, and an inducible mechanical injury to the vessel. We studied the effects of complement inhibitors against components of the lectin (MASP-1, MASP-2), classical (C1s), alternative (FD) and common pathways (C3, C5), as well as a novel triple fusion inhibitor of all three complement pathways (TriFu). Effects on clot formation were analysed by recording of fibrin deposition and the platelet activation marker CD62P at the injury site in real time using a confocal microscope. Results With the inhibitors targeting MASP-2 or C1s, no significant reduction of fibrin formation was observed, while platelet activation was significantly reduced in the presence of the FD inhibitor. Both common pathway inhibitors targeting C3 or C5, respectively, were associated with a substantial reduction of fibrin formation, and platelet activation was also reduced in the presence of the C3 inhibitor. Triple inhibition of all three activation pathways at the C3-convertase level by TriFu reduced both fibrin formation and platelet activation. When several complement inhibitors were directly compared in two individual donors, TriFu and the inhibitors of MASP-1 and C3 had the strongest effects on clot formation. Conclusion The observed impact of complement inhibition on reducing fibrin clot formation and platelet activation suggests a role of the complement system in haemostasis, with modulators of complement initiation, amplification or effector functions showing distinct profiles. While the interactions between complement and coagulation might have evolved to support haemostasis and protect against bleeding in case of vessel injury, they can turn harmful in pathological conditions when aggravating thromboinflammation and promoting thrombosis.
Integrated Analysis of Immune Infiltration and Hub Pyroptosis-Related Genes for Multiple Sclerosis

Purpose Studies on overall immune infiltration and pyroptosis in patients with multiple sclerosis (MS) are limited. This study explored immune cell infiltration and pyroptosis in MS using bioinformatics and experimental validation. Methods The GSE131282 and GSE135511 microarray datasets including brain autopsy tissues from controls and MS patients were downloaded for bioinformatic analysis. The gene expression-based deconvolution method, CIBERSORT, was used to determine immune infiltration. Differentially expressed genes (DEGs) and functional enrichments were analyzed. We then extracted pyroptosis-related genes (PRGs) from the DEGs by using machine learning strategies. Their diagnostic ability for MS was evaluated in both the training set (GSE131282 dataset) and validation set (GSE135511 dataset). In addition, messenger RNA (mRNA) expression of PRGs was validated using quantitative real-time polymerase chain reaction (qRT-PCR) in cortical tissue from an experimental autoimmune encephalomyelitis (EAE) model of MS. Moreover, the functional enrichment pathways of each hub PRG were estimated. Finally, co-expressed competitive endogenous RNA (ceRNA) networks of PRGs in MS were constructed. Results Among the infiltrating cells, naive CD4+ T cells (P=0.006), resting NK cells (P=0.002), activated mast cells (P=0.022), and neutrophils (P=0.002) were significantly higher in patients with MS than in controls. The DEGs of MS were screened. Analysis of enrichment pathways showed that the pathways of transcriptional regulatory mechanisms and ion channels associating with pyroptosis. Four PRGs genes CASP4, PLCG1, CASP9 and NLRC4 were identified. They were validated in both the GSE135511 dataset and the EAE model by using qRT-PCR. CASP4 and NLRC4 were ultimately identified as stable hub PRGs for MS. Single-gene Gene Set Enrichment Analysis showed that they mainly participated in biosynthesis, metabolism, and organism resistance. ceRNA networks containing CASP4 and NLRC4 were constructed. Conclusion MS was associated with immune infiltration. CASP4 and NLRC4 were key biomarkers of pyroptosis in MS.
Manipulation and the Allocational Role of Prices

Prices in secondary nancial markets are thought to play an important allocational role because they contain information that is relevant to the e¢ cient allocation of resources. This paper identi es a limitation inherent in this role of prices. It shows that when prices have an allocational role there can be opportunities for market manipulation. When prices a¤ect resource allocation they also a¤ect the true value of the underlying asset. This feedback from price to asset value enables an uninformed trader to generate a negative self-ful lling expectation by selling the asset short. The price impact of the short sales activity distorts asset allocation and further reduces the assets true value. The manipulator can then close out the short position at a pro t. It is shown that this type of manipulation is not pro table on the buy side, driving a wedge between the allocational implications of buy and sell side speculation. Moreover, when several agents with complemetary actions use the market price to infer information, the distortionary e¤ects of manipulation can be so strong as to render the existence of publicly observable prices undesirable.
High resolution 3D acquisition of the Olivier Strebelle's sculpture 'Athletes' Alley in Beijing 2008'

When an artist conceives a masterpiece, by working on a scaled model, their primary objective is to match the idea of what the art work should be, not to make a digital version that fulfills the engineering requirements. Olivier Strebelle's “Athletes' Alley” will be, in its final version, approximately 30 m tall and more than 100 m long and will be built in Beijing for the next Olympic games. We describe in this paper how we tackled the digitizing of the scaled model made by the artist in order to give the most precise digital model possible compatible with the engineering software. The original artist's model is made of stainless steel arcs welded together into a complex 3D wired light weight sculpture. The main challenges of this development were that no existing 3D scanner was adapted for this acquisition and that the time constraint to do the task was very short. Due to the model's complexity, we opted for a point-by-point acquisition with a high resolution surgical localizer, combined with an automatic filtering algorithm based on a priori knowledge of the sculpture. In summary we manage to perform the 3D geometry acquisition of a very complex structure by diverting the use of a medical 3D localizer where the commonly used 3D digitizers could not do the job. Copyright © 2007 Praise Worthy Prize S.r. l. All rights reserved.
A Centralized Task Allocation Algorithm for a Multi-Robot Inspection Mission With Sensing Specifications

Recently, considerable attention has focused on enhancing the security and safety of industries with high-risk level activities in order to protect the equipment and environment. In particular, chemical processes and nuclear power generation may have a deep impact on their surroundings. In the case of major events, such as chemical spills, oil rig explosions, or nuclear accidents, collecting accurate and rapidly evolving data becomes a challenging task. So, coordinating a fleet of autonomous mobile robots is a very promising way to deal with unpredicted events and also prevent malicious actions. This paper addresses the problem of assigning optimally a set of tasks to a set of mobile robots equipped with different sensors to minimize a global objective function. The robots perform sensing tasks in order to monitor the area and to facilitate firefighters and inspectors work if a disaster occurs by providing the necessary measures. For this purpose, a centralized Genetic Algorithm (GA) is proposed to determine the task each robot will perform and the order of execution. The proposed approach is tested through a simulation scenario of a grid map environment that represents an industrial area of the city of Le Havre, France. Moreover, a comparative study is conducted with the Hybrid Filtered Beam Search (HFBS) approach and the Mixed-Integer Linear Programming (MILP) solver Cplex. The results demonstrate that the GA approach offers a favorable balance between optimality and execution time.