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Ayahuasca is a plant concoction containing N,N-dimethyltryptamine (DMT) and certain β-carboline alkaloids from South America. Previous research in naturalistic settings has suggested that ingestion of ayahuasca can improve mental health and well-being; however, these studies were not placebo controlled and did not control for the possibility of expectation bias. This naturalistic observational study was designed to assess whether mental health changes were produced by ayahuasca or by set and setting. Assessments were made pre- and post-ayahuasca sessions in 30 experienced participants of ayahuasca retreats hosted in the Netherlands, Spain, and Germany. Participants consumed ayahuasca (N = 14) or placebo (N = 16). Analysis revealed a main effect of time on symptoms of depression, anxiety, and stress. Compared to baseline, symptoms reduced in both groups after the ceremony, independent of treatment. There was a main treatment × time interaction on implicit emotional empathy, indicating that ayahuasca increased emotional empathy to negative stimuli. The current findings suggest that improvements in mental health of participants of ayahuasca ceremonies can be driven by non-pharmacological factors that constitute a placebo response but also by pharmacological factors that are related to the use of ayahuasca. These findings stress the importance of placebo-controlled designs in psychedelic research and the need to further explore the contribution of non-pharmacological factors to the psychedelic experience.

Mangroves play an essential ecological role in the maintenance of the coastal zone and are extremely important for the socioeconomics of coastal communities. However, mangrove ecosystems are impacted by a range of anthropogenic pressures, and the loss of this habitat can be attributed primarily to the human occupation of the coastal zone. In the present study, we analyzed the spatial patterns of land use in the mangrove of the Brazilian Amazon coast, and evaluated the anthropogenic drivers of this impact, using a remote sensing approach. We mapped the road network using RapidEye images, and human settlements using global data. The results of these analyses indicate that the Brazilian Amazon coast has a low population density and low rates of anthropogenic impact in most of the coastal microregions investigated, factors that contribute to the maintenance and conservation of the region's mangrove. The study also revealed that the paved road network is one of the principal drivers of land use in the mangrove, whereas other factors, such as population density, urban centers, and the number of settlements are much less important. While the region has 2024 km of paved highways, unpaved roads (17,496 km) facilitate access to the mangrove, with approximately 90% of anthropogenic impact being recorded within a 3 km radius of these roads. While the network of paved highways is relatively reduced in extension, preventive measures are urgently required to impede any major shift in the current scenario, caused by the expansion of major development programs. The results of the study indicate that biophysical, economic, and political factors may also contribute to the reduction, stability, and development of one of the world's largest areas of mangrove forest.

The inflammatory hypothesis of schizophrenia (SZ) posits that inflammatory processes and neural–immune interactions are involved in its pathogenesis, and may underpin some of its neurobiological correlates. SZ is the psychiatric disorder causing the most severe burden of illness, not just owing to its psychiatric impairment, but also owing to its significant medical comorbidity. C-reactive protein (CRP) is a commonly used biomarker of systemic inflammation worldwide. There are some conflicting results regarding the behaviour of CRP in SZ. The aims of this study were to verify whether peripheral CRP levels are indeed increased in SZ, whether different classes of antipsychotics divergently modulate CRP levels and whether its levels are correlated with positive and negative symptomatology. With that in mind, we performed a meta-analysis of all cross-sectional studies of serum and plasma CRP levels in SZ compared to healthy subjects. In addition, we evaluated longitudinal studies on CRP levels before and after antipsychotic use. Our meta-analyses of CRP in SZ included a total of 26 cross-sectional or longitudinal studies comprising 85 000 participants. CRP levels were moderately increased in persons with SZ regardless of the use of antipsychotics and did not change between the first episode of psychosis and with progression of SZ ( g =0.66, 95% confidence interval (95% CI) 0.43 to 0.88, P <0.001, 24 between-group comparisons, n =82 962). The extent of the increase in peripheral CRP levels paralleled the increase in severity of positive symptoms, but was unrelated to the severity of negative symptoms. CRP levels were also aligned with an increased body mass index. Conversely, higher age correlated with a smaller difference in CRP levels between persons with SZ and controls. Furthermore, CRP levels did not increase after initiation of antipsychotic medication notwithstanding whether these were typical or atypical antipsychotics ( g =0.01, 95% CI −0.20 to 0.22, P =0.803, 8 within-group comparisons, n =713). In summary, our study provides further evidence of the inflammatory hypothesis of SZ. Whether there is a causal relationship between higher CRP levels and the development of SZ and aggravation of psychotic symptoms, or whether they are solely a marker of systemic low-grade inflammation in SZ, remains to be clarified.

It has been postulated that schizophrenia (SZ) is related to a lower expression of brain-derived neurotrophic factor (BDNF). In the past few years, an increasing number of divergent clinical studies assessing BDNF in serum and plasma have been published. It is now possible to verify the relationship between BDNF levels and severity of symptoms in SZ as well as the effects of antipsychotic drugs on BDNF using meta-analysis. The aims of this study were to verify if peripheral BDNF is decreased in SZ, whether its levels are correlated with positive and negative symptomatology and if BDNF levels change after antipsychotic treatment. This report consists of two distinct meta-analyses of peripheral BDNF in SZ including a total of 41 studies and more than 7000 participants: (1) peripheral BDNF levels in serum and plasma were moderately reduced in SZ compared with controls. Notably, this decrease was accentuated with the disease duration. However, the extent of peripheral BDNF level decrease did not correlate with the severity of positive and negative symptoms. (2) In plasma, but not serum, peripheral BDNF levels are consistently increased after antipsychotic treatment irrespective of the patient’s response to medication. In conclusion, there is compelling evidence that there are decreased levels of peripheral BDNF in SZ, in parallel to previously described reduced cerebral BDNF expression. It remains unclear whether these systemic changes are causally related to the development of SZ or if they are merely a pathologic epiphenomenon.

Key message We compare genomic selection methods that use correlated traits to help predict biomass yield in sorghum, and find that trait-assisted genomic selection performs best.Genomic selection (GS) is usually performed on a single trait, but correlated traits can also help predict a focal trait through indirect or multi-trait GS. In this study, we use a pre-breeding population of biomass sorghum to compare strategies that use correlated traits to improve prediction of biomass yield, the focal trait. Correlated traits include moisture, plant height measured at monthly intervals between planting and harvest, and the area under the growth progress curve. In addition to single- and multi-trait direct and indirect GS, we test a new strategy called trait-assisted GS, in which correlated traits are used along with marker data in the validation population to predict a focal trait. Single-trait GS for biomass yield had a prediction accuracy of 0.40. Indirect GS performed best using area under the growth progress curve to predict biomass yield, with a prediction accuracy of 0.37, and did not differ from indirect multi-trait GS that also used moisture information. Multi-trait GS and single-trait GS yielded similar results, indicating that correlated traits did not improve prediction of biomass yield in a standard GS scenario. However, trait-assisted GS increased prediction accuracy by up to 50% when using plant height in both the training and validation populations to help predict yield in the validation population. Coincidence between selected genotypes in phenotypic and genomic selection was also highest in trait-assisted GS. Overall, these results suggest that trait-assisted GS can be an efficient strategy when correlated traits are obtained earlier or more inexpensively than a focal trait.

Background Centromeres load kinetochore complexes onto chromosomes, which mediate spindle attachment and allow segregation during cell division. Although centromeres perform a conserved cellular function, their underlying DNA sequences are highly divergent within and between species. Despite variability in DNA sequence, centromeres are also universally suppressed for meiotic crossover recombination, across eukaryotes. However, the genetic and epigenetic factors responsible for suppression of centromeric crossovers remain to be completely defined. Results To explore the centromere-proximal meiotic recombination landscape, we map 14,397 crossovers against fully assembled Arabidopsis thaliana ( A. thaliana ) genomes. A. thaliana centromeres comprise megabase satellite repeat arrays that load nucleosomes containing the CENH3 histone variant. Each chromosome contains a structurally polymorphic region of ~3–4 megabases, which lack crossovers and include the satellite arrays. This polymorphic region is flanked by ~1–2 megabase low-recombination zones. These recombination-suppressed regions are enriched for Gypsy/Ty3 retrotransposons, and additionally contain expressed genes with high genetic diversity that initiate meiotic recombination, yet do not crossover. We map crossovers at high-resolution in proximity to CEN3 , which resolves punctate centromere-proximal hotspots that overlap gene islands embedded in heterochromatin. Centromeres are densely DNA methylated and the recombination landscape is remodelled in DNA methylation mutants. We observe that the centromeric low-recombining zones decrease and increase crossovers in CG ( met1 ) and non-CG ( cmt3 ) mutants, respectively, whereas the core non-recombining zones remain suppressed. Conclusion Our work relates the genetic and epigenetic organization of A. thaliana centromeres and flanking pericentromeric heterochromatin to the zones of crossover suppression that surround the CENH3-occupied satellite repeat arrays.

Background Advances in genotyping and phenotyping techniques have enabled the acquisition of a great amount of data. Consequently, there is an interest in multivariate statistical analyses that identify genomic regions likely to contain causal mutations affecting multiple traits (i.e., pleiotropy). As the demand for multivariate analyses increases, it is imperative that optimal tools are available to assess their performance. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R/CRAN package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in a wide range of genetic architectures, including additive, dominance and epistatic models. Results We illustrate simplePHENOTYPES’ ability to simulate thousands of phenotypes in less than one minute. We then provide two vignettes illustrating how to simulate sets of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities. Conclusions simplePHENOTYPES is a R/CRAN package that makes it possible to simulate multiple traits controlled by loci with varying degrees of pleiotropy. Its ability to interface with both commonly-used marker data formats and downstream quantitative genetics software and packages should facilitate a rigorous assessment of both existing and emerging statistical GWAS and GS approaches. simplePHENOTYPES is also available at https://github.com/samuelbfernandes/simplePHENOTYPES .

This study proposes a time-aware predictive maintenance framework for railway infrastructure, that operates under sparse time-series conditions. Addressing the challenge of low-frequency and irregular monitoring, the method integrates normalized Track Quality Indices (TQI) with lightweight classical regressors and Long Short-Term Memory (LSTM) networks, to estimate Health State (HS) trajectories and forecast Remaining Useful Life (RUL) at multiple degradation thresholds. The framework leverages statistical modeling and confidence bounds to account for measurement noise and uncertainty, enabling interpretable and probabilistic RUL predictions that align with physical degradation dynamics. Unlike dense-data or binary-threshold approaches, this framework enables risk-informed maintenance planning by modeling degradation progression across multiple stages. Experimental validation was conducted using real-world data collected over one year from a national freight railway operator in Brazil, obtained via a sensor-equipped diagnostic wagon. Results demonstrate the framework’s capacity to detect regime changes in track degradation, anticipate threshold crossings (e.g., 80%, 50%, 20%, and 0% HS), and support prioritized interventions under uncertainty. The proposed method proves effective in capturing nonlinear, accelerated degradation, even under limited data granularity. It offers a scalable and operationally feasible solution for asset managers seeking to optimize long-term infrastructure resilience and lifecycle cost. By bridging models with field-applicable insights, this work contributes to the advancement of predictive maintenance practices for safety-critical transportation networks.

Riparian forests are important for maintaining aquatic biodiversity, yet they face increasing pressure from logging activities. This study assessed the functional diversity of Ephemeroptera, Plecoptera, and Trichoptera (EPT) in 30 Amazonian first-order streams across three riparian forests: pristine, selectively logged, and conventionally logged. We evaluated four habitat attributes linked to ecosystem functioning (canopy cover, water temperature, sediment organic matter, and small woody debris) and calculated two indices of functional diversity: richness and divergence. Functional diversity was highest in pristine streams, intermediate in selectively logged streams, and lowest in conventionally logged streams. Functional richness and divergence declined significantly in conventionally logged forests, indicating a loss of ecological traits and potential reductions in ecosystem functions. We also observed that canopy cover, sediment organic matter, and woody debris were positively associated with EPT functional diversity, while water temperature had a negative association. These findings highlight that conventional logging leads to the functional homogenization of aquatic insect assemblages, compromising key ecological processes. Selective logging that maintains riparian buffers may preserve functional diversity, even though these differences may be influenced by site-specific environmental conditions. Our results underscore the importance of conserving riparian integrity to sustain the resilience and functioning of tropical stream ecosystems in logged landscapes.