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Isoprene (C 5 H 8 ) is the non-methane hydrocarbon with the highest emissions to the atmosphere. It is mainly produced by vegetation, especially broad-leaved trees, and efficiently transported to the upper troposphere in deep convective clouds, where it is mixed with lightning NO x . Isoprene oxidation products drive rapid formation and growth of new particles in the tropical upper troposphere. However, isoprene oxidation pathways at low temperatures are not well understood. Here, in experiments at the CERN CLOUD chamber at 223 K and 243 K, we find that isoprene oxygenated organic molecules (IP-OOM) all involve two successive OH ∙ oxidations. However, depending on the ambient concentrations of the termination radicals ( HO 2 ∙ , NO ∙ , and NO 2 ∙ ), vastly-different IP-OOM emerge, comprising compounds with zero, one or two nitrogen atoms. Our findings indicate high IP-OOM production rates for the tropical upper troposphere, mainly resulting in nitrate IP-OOM but with an increasing non-nitrate fraction around midday, in close agreement with aircraft observations. Experiments under upper-tropospheric conditions map the chemical formation of isoprene oxygenated organic molecules (important molecules for new particle formation) and reveal that relative radical ratios control their composition

During summer, ammonia emissions in Southeast Asia influence air pollution and cloud formation. Convective transport by the South Asian monsoon carries these pollutant air masses into the upper troposphere and lower stratosphere (UTLS), where they accumulate under anticyclonic flow conditions. This air mass accumulation is thought to contribute to particle formation and the development of the Asian Tropopause Aerosol Layer (ATAL). Despite the known influence of ammonia and particulate ammonium on air pollution, a comprehensive understanding of the ATAL is lacking. In this modelling study, the influence of ammonia on particle formation is assessed with emphasis on the ATAL. We use the EMAC chemistry-climate model, incorporating new particle formation parameterisations derived from experiments at the CERN CLOUD chamber. Our diurnal cycle analysis confirms that new particle formation mainly occurs during daylight, with a 10-fold enhancement in rate. This increase is prominent in the South Asian monsoon UTLS, where deep convection introduces high ammonia levels from the boundary layer, compared to a baseline scenario without ammonia. Our model simulations reveal that this ammonia-driven particle formation and growth contributes to an increase of up to 80% in cloud condensation nuclei (CCN) concentrations at cloud-forming heights in the South Asian monsoon region. We find that ammonia profoundly influences the aerosol mass and composition in the ATAL through particle growth, as indicated by an order of magnitude increase in nitrate levels linked to ammonia emissions. However, the effect of ammonia-driven new particle formation on aerosol mass in the ATAL is relatively small. Ammonia emissions enhance the regional aerosol optical depth (AOD) for shortwave solar radiation by up to 70%. We conclude that ammonia has a pronounced effect on the ATAL development, composition, the regional AOD, and CCN concentrations.

Aircraft observations have revealed ubiquitous new particle formation in the tropical upper troposphere over the Amazon 1 , 2 and the Atlantic and Pacific oceans 3 , 4 . Although the vapours involved remain unknown, recent satellite observations have revealed surprisingly high night-time isoprene mixing ratios of up to 1 part per billion by volume (ppbv) in the tropical upper troposphere 5 . Here, in experiments performed with the CERN CLOUD (Cosmics Leaving Outdoor Droplets) chamber, we report new particle formation initiated by the reaction of hydroxyl radicals with isoprene at upper-tropospheric temperatures of −30 °C and −50 °C. We find that isoprene-oxygenated organic molecules (IP-OOM) nucleate at concentrations found in the upper troposphere, without requiring any more vapours. Moreover, the nucleation rates are enhanced 100-fold by extremely low concentrations of sulfuric acid or iodine oxoacids above 10 5  cm −3 , reaching rates around 30 cm −3  s −1 at acid concentrations of 10 6  cm −3 . Our measurements show that nucleation involves sequential addition of IP-OOM, together with zero or one acid molecule in the embryonic molecular clusters. IP-OOM also drive rapid particle growth at 3–60 nm h −1 . We find that rapid nucleation and growth rates persist in the presence of NO x at upper-tropospheric concentrations from lightning. Our laboratory measurements show that isoprene emitted by rainforests may drive rapid new particle formation in extensive regions of the tropical upper troposphere 1 , 2 , resulting in tens of thousands of particles per cubic centimetre. Experiments performed in the CERN CLOUD chamber show that, under upper-tropospheric conditions, new atmospheric particle formation may be initiated by the reaction of hydroxyl radicals with isoprene emitted by rainforests.

Breast cancer is a heterogeneous disease with distinct molecular subtypes including the aggressive subtype triple-negative breast cancer (TNBC). We compared blood-borne miRNA signatures of early-stage basal-like (cytokeratin-CK5-positive) TNBC patients to age-matched controls. The miRNAs of TNBC patients were assessed prior to and following platinum-based neoadjuvant chemotherapy (NCT). After an exploratory genome-wide study on 21 cases and 21 controls using microarrays, the identified signatures were verified independently in two laboratories on the same and a new cohort by RT-qPCR. We differentiated the blood of TNBC patients before NCT from controls with 84% sensitivity. The most significant miRNA for this diagnostic classification was miR-126-5p (two tailed t-test p-value of 1.4 × 10 −5 ). Validation confirmed the microarray results for all tested miRNAs. Comparing cancer patients prior to and post NCT highlighted 321 significant miRNAs (among them miR-34a, p-value of 1.2 × 10 −23 ). Our results also suggest that changes in miRNA expression during NCT may have predictive potential to predict pathological complete response (pCR). In conclusion we report that miRNA expression measured from blood facilitates early and minimally-invasive diagnosis of basal-like TNBC. We also demonstrate that NCT has a significant influence on miRNA expression. Finally, we show that blood-borne miRNA profiles monitored over time have potential to predict pCR.

Gut microbiota plays an essential role in health and disease. It is constantly evolving and in permanent communication with its host. The gut microbiota is increasingly seen as an organ, and its failure, reflected by dysbiosis, is seen as an organ failure associated with poor outcomes. Critically ill patients may have an altered gut microbiota, namely dysbiosis, with a severe reduction in “health-promoting” commensal intestinal bacteria (such as Firmicutes or Bacteroidetes) and an increase in potentially pathogenic bacteria (e.g. Proteobacteria). Many factors that occur in critically ill patients favour dysbiosis, such as medications or changes in nutrition patterns. Dysbiosis leads to several important effects, including changes in gut integrity and in the production of metabolites such as short-chain fatty acids and trimethylamine N-oxide. There is increasing evidence that gut microbiota and its alteration interact with other organs, highlighting the concept of the gut–organ axis. Thus, dysbiosis will affect other organs and could have an impact on the progression of critical diseases. Current knowledge is only a small part of what remains to be discovered. The precise role and contribution of the gut microbiota and its interactions with various organs is an intense and challenging research area that offers exciting opportunities for disease prevention, management and therapy, particularly in critical care where multi-organ failure is often the focus. This narrative review provides an overview of the normal composition of the gut microbiota, its functions, the mechanisms leading to dysbiosis, its consequences in an intensive care setting, and highlights the concept of the gut–organ axis.

Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins ( SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study. Endometrial cancer is the most common invasive gynaecological cancer in developed countries. Here a meta-analysis identifies an additional nine novel endometrial cancer risk loci and eQTL analysis reveals risk variants associate with reduced expression of negative regulators of oncogenic signal transduction proteins.

Background Difficulties in social communication are a defining clinical feature of autism. However, the underlying neurobiological heterogeneity has impeded targeted therapies and requires new approaches to identifying clinically relevant bio-behavioural subgroups. In the largest autism cohort to date, we comprehensively examined difficulties in facial expression recognition, a key process in social communication, as a bio-behavioural stratification biomarker, and validated them against clinical features and neurofunctional responses. Methods Between 255 and 488 participants aged 6–30 years with autism, typical development and/or mild intellectual disability completed the Karolinska Directed Emotional Faces task, the Reading the Mind in the Eyes Task and/or the Films Expression Task. We first examined mean-group differences on each test. Then, we used a novel intersection approach that compares two centroid and connectivity-based clustering methods to derive subgroups based on the combined performance across the three tasks. Measures and subgroups were then related to clinical features and neurofunctional differences measured using fMRI during a fearful face-matching task. Results We found significant mean-group differences on each expression recognition test. However, cluster analyses showed that these were driven by a low-performing autistic subgroup (~ 30% of autistic individuals who performed below 2SDs of the neurotypical mean on at least one test), while a larger subgroup (~ 70%) performed within 1SD on at least 2 tests. The low-performing subgroup also had on average significantly more social communication difficulties and lower activation in the amygdala and fusiform gyrus than the high-performing subgroup. Limitations Findings of autism expression recognition subgroups and their characteristics require independent replication. This is currently not possible, as there is no other existing dataset that includes all relevant measures. However, we demonstrated high internal robustness (91.6%) of findings between two clustering methods with fundamentally different assumptions, which is a critical pre-condition for independent replication. Conclusions We identified a subgroup of autistic individuals with expression recognition difficulties and showed that this related to clinical and neurobiological characteristics. If replicated, expression recognition may serve as bio-behavioural stratification biomarker and aid in the development of targeted interventions for a subgroup of autistic individuals.

Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale ( N  = 7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals’ transdiagnostic functioning. Taken together, we demonstrate that normative modelling of fMRI task-activation can be used to illustrate the influence of different task choices and map replicable individual differences, and we encourage its application to other neuroimaging tasks in future studies. Normative modelling of fMRI task-activation characterizes the influence of different task choices, and provides a method to map replicable individual differences and their multivariate associations with transdiagnostic symptom domains.

This paper reports on the design and evaluation of Field Studies in Functional Ecology (FSFE), a two-week intensive residential field course that enables students to master core content in functional ecology alongside skills that facilitate their transition from “student” to “scientist.” We provide an overview of the course structure, showing how the constituent elements have been designed and refined over successive iterations of the course. We detail how FSFE students: (1) Work closely with discipline specialists to develop a small group project that tests an hypothesis to answer a genuine scientific question in the field; (2) Learn critical skills of data management and communication; and (3) Analyze, interpret, and present their results in the format of a scientific symposium. This process is repeated in an iterative “cognitive apprenticeship” model, supported by a series of workshops that name and explicitly instruct the students in “hard” and “soft” skills (e.g., statistics and teamwork, respectively) critically relevant for research and other careers. FSFE students develop a coherent and nuanced understanding of how to approach and execute ecological studies. The sophisticated knowledge and ecological research skills that they develop during the course is demonstrated through high-quality presentations and peer-reviewed publications in an open-access, student-led journal. We outline our course structure and evaluate its efficacy to show how this novel combination of field course elements allows students to gain maximum value from their educational journey, and to develop cognitive, affective, and reflective tools to help apply their skills as scientists.