Total RNA analysis of the active microbiome on moving bed biofilm reactor carriers under incrementally increasing micropollutant concentrations.

Micropollutants are increasingly prevalent in the aquatic environment. A major part of these originates from wastewater treatment plants since traditional treatment technologies do not remove micropollutants sufficiently. Moving bed biofilm reactors (MBBRs), however, have been shown to aid in micropollutant removal when applied to conventional wastewater treatment as a polishing step. Here, we used Total RNA sequencing to investigate both the active microbial community and functional dynamics of MBBR biofilms when these were exposed to increasing micropollutant concentrations over time. Concurrently, we conducted batch culture experiments using biofilm carriers from the MBBRs to assess micropollutant degradation potential. Our study showed that biofilm eukaryotes, in particular protozoa, were negatively influenced by micropollutant exposure, in contrast to prokaryotes that increased in relative abundance. Further, we found several functional genes that were differentially expressed between the MBBR with added micropollutants and the control. These include genes involved in aromatic and xenobiotic compound degradation. Moreover, the biofilm carrier batch experiment showed vastly different alterations in benzotriazole and diclofenac degradation following the increased micropollutant concentrations in the MBBR. Ultimately, this study provides essential insights into the microbial community and functional dynamics of MBBRs and how an increased load of micropollutants influences these dynamics.

Exposure-response modeling for nausea incidence for cotadutide using a Markov modeling approach.

Cotadutide is a dual glucagon-like peptide-1 (GLP-1)/glucagon receptor agonist. Gastrointestinal adverse effects are known to be associated with GLP-1 receptor agonism and can be mitigated through tolerance development via a gradual up-titration. This analysis aimed to characterize the relationship between exposure and nausea incidence and to optimize titration schemes. The model was developed with pooled data from cotadutide-administrated studies. Three different modeling approaches, proportional odds (PO), discrete-time Markov, and two-stage discrete-time Markov models, were employed to characterize the exposure-nausea relationship. The severity of nausea was modeled as different states (non-nausea, mild, and moderate/severe). The most appropriate model was selected to perform the covariate analysis, and the final covariate model was used to simulate the nausea event rates for various titration scenarios. The two Markov models demonstrated comparable performance and were better than the PO model. The covariate analysis was conducted with the standard Markov model for operational simplification and identified disease indications (NASH, obesity) and sex as covariates on Markov parameters. The simulations indicated that the biweekly titration with twofold dose escalation is superior to other titration schemes with a relatively low predicted nausea event rate at 600 µg (25%) and a shorter titration interval (8 weeks) to reach the therapeutic dose. The model can be utilized to optimize starting dose and titration schemes for other therapeutics in clinical trials to achieve an optimal risk-benefit balance and reach the therapeutic dose with minimal titration steps.

Airborne DNA reveals predictable spatial and seasonal dynamics of fungi.

Fungi are among the most diverse and ecologically important kingdoms in life. However, the distributional ranges of fungi remain largely unknown as do the ecological mechanisms that shape their distributions1,2. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardized aerial sampling of fungal spores3. The vast majority of operational taxonomic units were detected within only one climatic zone, and the spatiotemporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating for only a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with a microbial lifestyle follow the global biodiversity paradigms known for macroorganisms4,5.

A novel genus of Pectobacterium bacteriophages display broad host range by targeting several species of Danish soft rot isolates.

The bacterial diseases black leg and soft rot in potatoes cause heavy losses of potatoes worldwide. Bacteria within the genus Pectobacteriaceae are the causative agents of black leg and soft rot. The use of antibiotics in agriculture is heavily regulated and no other effective treatment currently exists, but bacteriophages (phages) have shown promise as potential biocontrol agents. In this study we isolated soft rot bacteria from potato tubers and plant tissue displaying soft rot or black leg symptoms collected in Danish fields. We then used the isolated bacterial strains as hosts for phage isolation. Using organic waste, we isolated phages targeting different species within Pectobacterium. Here we focus on seven of these phages representing a new genus primarily targeting P. brasiliense; phage Ymer, Amona, Sabo, Abuela, Koroua, Taid and Pappous. TEM image of phage Ymer showed siphovirus morphotype, and the proposed Ymer genus belongs to the class Caudoviricetes, with double-stranded DNA genomes varying from 39 kb to 43 kb. In silico host range prediction using a CRISPR-Cas spacer database suggested both P. brasiliense, P. polaris and P. versatile as natural hosts for phages within the proposed Ymer genus. A following host range experiment, using 47 bacterial isolates from Danish tubers and plants symptomatic with soft rot or black leg disease verified the in silico host range prediction, as the genus as a group were able to infect all three Pectobacterium species. Phages did, however, primarily target P. brasiliense isolates and displayed differences in host range even within the species level. Two of the phages were able to infect two or more Pectobacterium species. Despite no nucleotide similarity with any phages in the NCBI database, the proposed Ymer genus did share some similarity at the protein level, as well as gene synteny, with currently known phages. None of the phages encoded integrases or other genes typically associated with lysogeny. Similarly, no virulence factors nor antimicrobial resistance genes were found, and combined with their ability to infect several soft rot-causing Pectobacterium species from Danish fields, demonstrates their potential as biocontrol agents against soft rot and black leg diseases in potatoes.

21st century accelerating neurological deaths in UK and major Western countries: - Demographic and/or multiple-interactive-environmental causes?

OBJECTIVE: We examine whether the rise in neurological death rates over the 21st century are solely explained by the Gompertzian hypothesis. STUDY DESIGN: We examine two data-sets. First, Office of National Statistics (ONS, 2022) for nineteen mortality categories in England/Wales, including Alzheimer's, Dementias and Parkinson's Disease. Secondly, WHO (2020) Combined Neurological Mortality (CNM), from WHO Global mortality categories, Nervous Disease Deaths, and Alzheimer's & Other Dementias. METHODS: Based on ONS data we investigate trends in Age-Standardised Mortality Rates (ASMR) of CNM 2000-2022. Based on WHO data we examine rates of Early Deaths (55-74) and ASMR, for CNM between 2000 and 2015 in the ten Major 'Western' economies: Australia, Canada, France, Germany, Italy, Japan, Netherlands, Spain, UK, and the USA. RESULTS: In England & Wales death rates have increased 348% for Alzheimer's, 235% for Dementias, and 105% for Parkinson's Disease in contrast with falls in most other cause mortality. Early Adults Deaths CNM rates increased in eight countries, an average of 19%. Neurological ASMR rose in every country, averaging 43%, the highest was the UK 95%. CONCLUSION: We reject the Gompertzian hypothesis as an all-encompassing explanation for these marked increases in ASMR. Increases in early adult neurological deaths suggests this cannot be solely explained by an aging population. Furthermore, increases in mortality could be related to an increased prevalence of neurological conditions in this age group. Action is urgently needed to investigate factors - whether environmental, lifestyle or health systems - that could explain these findings.

Gut microbiota-mediated polyphenol metabolism is restrained by parasitic whipworm infection and associated with altered immune function in mice.

Polyphenols are phytochemicals commonly found in plant-based diets which have demonstrated immunomodulatory and anti-inflammatory properties. However, the interplay between polyphenols and pathogens at mucosal barrier surfaces has not yet been elucidated in detail. Here, we show that proanthocyanidin (PAC) polyphenols interact with gut parasites to influence immune function and gut microbial-derived metabolites in mice. PAC intake inhibited mastocytosis during infection with the small intestinal roundworm Heligmosomoides polygyrus, and altered the host tissue transcriptome at the site of infection with the large intestinal whipworm Trichuris muris, with a notable enhancement of type-1 inflammatory and interferon-driven gene pathways. In the absence of infection, PAC intake promoted the expansion of Turicibacter within the gut microbiota, increased fecal short chain fatty acids, and enriched phenolic metabolites such as phenyl-γ-valerolactones in the cecum. However, these putatively beneficial effects were reduced in PAC-fed mice infected with T. muris, suggesting concomitant parasite infection can attenuate gut microbial-mediated PAC catabolism. Collectively, our results suggest an inter-relationship between a phytonutrient and infection, whereby PAC may augment parasite-induced inflammation (most prominently with the cecum dwelling T. muris), and infection may abrogate the beneficial effects of health-promoting phytochemicals.

Image classification with symbolic hints using limited resources.

Typical machine learning classification benchmark problems often ignore the full input data structures present in real-world classification problems. Here we aim to represent additional information as "hints" for classification. We show that under a specific realistic conditional independence assumption, the hint information can be included by late fusion. In two experiments involving image classification with hints taking the form of text metadata, we demonstrate the feasibility and performance of the fusion scheme. We fuse the output of pre-trained image classifiers with the output of pre-trained text models. We show that calibration of the pre-trained models is crucial for the performance of the fused model. We compare the performance of the fusion scheme with a mid-level fusion scheme based on support vector machines and find that these two methods tend to perform quite similarly, albeit the late fusion scheme has only negligible computational costs.

Non-target analysis of Danish wastewater treatment plant effluent: Statistical analysis of chemical fingerprinting as a step toward a future monitoring tool.

In an attempt to discover and characterize the plethora of xenobiotic substances, this study investigates chemical compounds released into the environment with wastewater effluents. A novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a newly optimized data-processing pipeline were applied to effluent samples from two state-of-the-art and one small wastewater treatment facility. In total, 785 molecular structures were obtained, of which 38 were identified as single compounds, while 480 structures were identified at a putative level. Most of these substances were therapeutics and drugs, present as parent compounds and metabolites. Using R packages Phyloseq and MetacodeR, originally developed for bioinformatics, significant differences in xenobiotic presence in the wastewater effluents between the three sites were demonstrated.

Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA.

Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m3 of air. We applied a conservative bioinformatics pipeline that filtered out sequences that did not show strong evidence of representing a fungal species. The pipeline yielded 27,954 species-level operational taxonomic units (OTUs). Each OTU is accompanied by a probabilistic taxonomic classification, validated through comparison with expert evaluations. To examine the potential of the data for ecological analyses, we partitioned the variation in species distributions into spatial and seasonal components, showing a strong effect of the annual mean temperature on community composition.

Dual glucagon-like peptide-1 and glucagon receptor agonism reduces energy intake in type 2 diabetes with obesity.

AIMS: To establish which components of energy balance mediate the clinically significant weight loss demonstrated with use of cotadutide, a glucagon-like peptide-1 (GLP-1)/glucagon receptor dual agonist, in early-phase studies. MATERIALS AND METHODS: We conducted a phase 2a, single-centre, randomized, placebo-controlled trial in overweight and obese adults with type 2 diabetes. Following a 16-day single-blind placebo run-in, participants were randomized 2:1 to double-blind 42-day subcutaneous treatment with cotadutide (100-300 µg daily) or placebo. The primary outcome was percentage weight change. Secondary outcomes included change in energy intake (EI) and energy expenditure (EE). RESULTS: A total of 12 participants (63%) in the cotadutide group and seven (78%) in the placebo group completed the study. The mean (90% confidence interval [CI]) weight change was -4.0% (-4.9%, -3.1%) and -1.4% (-2.7%, -0.1%) for the cotadutide and placebo groups, respectively (p = 0.011). EI was lower with cotadutide versus placebo (-41.3% [-66.7, -15.9]; p = 0.011). Difference in EE (per kJ/kg lean body mass) for cotadutide versus placebo was 1.0% (90% CI -8.4, 10.4; p = 0.784), assessed by doubly labelled water, and -6.5% (90% CI -9.3, -3.7; p < 0.001), assessed by indirect calorimetry. CONCLUSION: Weight loss with cotadutide is primarily driven by reduced EI, with relatively small compensatory changes in EE.

Environmental drivers of Arctic communities based on metabarcoding of marine sediment eDNA.

Our ability to assess biodiversity at relevant spatial and temporal scales for informing management is of increasing importance given this is foundational to identify and mitigate the impacts of global change. Collecting baseline information and tracking ecological changes are particularly important for areas experiencing rapid changes and representing data gaps such as Arctic marine ecosystems. Environmental DNA has the potential to provide such data. We extracted environmental DNA from 90 surface sediment samples to assess eukaryote diversity around Greenland and Svalbard using two separate primer pairs amplifying different sections of the 18S rRNA gene. We detected 27 different phyla and 99 different orders and found that temperature and the change in temperature explained the most variation in the community in a single linear model, while latitude, sea ice cover and change in temperature explained the most variation in the community when assessed by individual non-linear models. We identified potential indicator taxa for Arctic climate change, including a terebellid annelid worm. In conclusion, our study demonstrates that environmental DNA offers a feasible method to assess biodiversity and identifies warming as a key driver of differences in biodiversity across these remote ecosystems.

Using sequences of life-events to predict human lives.

Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions.

Terapia comportamental cognitiva para la esquizofrenia / Cognitive behavioral therapy for schizophrenia

El presente trabajo evidencia la eficacia de la terapia comportamental-cognitiva en un amplio rango de trastornos mentales, incluyendo la psicosis. Su crecimiento y difusión se debe a que está enmarcada dentro de las terapias directivas breves por lo que la ecuación costo-eficacia se ve altamente favorecida. La TCC presume que la formación de creencias anómalas genera experiencias psicóticas. Se presenta el modelo ABC que refiere que los delirios de las voces son producto de creencias y no de hechos. El tratamiento que propone consta de una serie de estadios: evaluación, compromiso, establecimiento de objetivos, normalización, formulación, análisis crítico colaborativo, desarrollo de explicaciones alternativas, prevención de recaída, finalización, y por último, la supervisión. Cada uno de ellos incluye problemas potenciales que pueden presentarse. Finalmente se expone un caso clínico a modo ilustrativo del tratamiento descripto