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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.

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.

Maritta Välimäki,1–3 Lauri Kuosmanen,1,4,5 Heli Hätönen,1 Marita Koivunen,1,6 Anneli Pitkänen,7 Christina Athanasopoulou,1 Minna Anttila1 1Department of Nursing Science, Faculty of Medicine, University of Turku, Finland; 2Development Unit, Turku University Hospital, Turku, Finland; 3School of Nursing, Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR, China; 4University of Helsinki and Helsinki University Hospital, Helsinki, Finland; 5Social and Healthcare Department, City of Vantaa, Vantaa, Finland; 6Administrative Centre, Research and Development, Satakunta Hospital District, Pori, Finland; 7Administration Centre, Pirkanmaa Hospital District, Tampere, Finland Purpose: Information and communication technologies have been developed for a variety of health care applications and user groups in the field of health care. This study examined the connectivity to computers and the Internet among patients with schizophrenia spectrum disorders (SSDs).Patients and methods: A cross-sectional survey design was used to study 311 adults with SSDs from the inpatient units of two psychiatric hospitals in Finland. The data collection lasted for 20 months and was done through patients’ medical records and a self-reported, structured questionnaire. Data analysis included descriptive statistics.Results: In total, 297 patients were included in this study (response rate =96%). More than half of them (n=156; 55%) had a computer and less than half of them (n=127; 44%) had the Internet at home. Of those who generally had access to computers and the Internet, more than one-fourth (n=85; 29%) used computers daily, and >30% (n=96; 33%) never accessed the Internet. In total, approximately one-fourth of them (n=134; 25%) learned to use computers, and less than one-third of them (n=143; 31%) were known to use the Internet by themselves. Older people (aged 45–65 years) and those with less years of education (primary school) tended not to use the computers and the Internet at all (P

ObjectiveThis study was undertaken to assess reproducibility of the epilepsy outcome and phenotype in a lateral fluid percussion model of posttraumatic epilepsy (PTE) across three study sites.MethodsA total of 525 adult male Sprague Dawley rats were randomized to lateral fluid percussion-induced brain injury (FPI) or sham operation. Of these, 264 were assigned to magnetic resonance imaging (MRI cohort, 43 sham, 221 traumatic brain injury [TBI]) and 261 to electrophysiological follow-up (EEG cohort, 41 sham, 220 TBI). A major effort was made to harmonize the rats, materials, equipment, procedures, and monitoring systems. On the 7th post-TBI month, rats were video-EEG monitored for epilepsy diagnosis.ResultsA total of 245 rats were video-EEG phenotyped for epilepsy on the 7th postinjury month (121 in MRI cohort, 124 in EEG cohort). In the whole cohort (n = 245), the prevalence of PTE in rats with TBI was 22%, being 27% in the MRI and 18% in the EEG cohort (p > .05). Prevalence of PTE did not differ between the three study sites (p > .05). The average seizure frequency was .317 ± .725 seizures/day at University of Eastern Finland (UEF; Finland), .085 ± .067 at Monash University (Monash; Australia), and .299 ± .266 at University of California, Los Angeles (UCLA; USA; p  .05). Of the 219 seizures, 53% occurred as part of a seizure cluster (≥3 seizures/24 h; p >.05 between the study sites). Of the 209 seizures, 56% occurred during lights-on period and 44% during lights-off period (p > .05 between the study sites).SignificanceThe PTE phenotype induced by lateral FPI is reproducible in a multicenter design. Our study supports the feasibility of performing preclinical multicenter trials in PTE to increase statistical power and experimental rigor to produce clinically translatable data to combat epileptogenesis after TBI.

ObjectiveProject 1 of the Preclinical Multicenter Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) consortium aims to identify preclinical biomarkers for antiepileptogenic therapies following traumatic brain injury (TBI). The international participating centers in Finland, Australia, and the United States have made a concerted effort to ensure protocol harmonization. Here, we evaluate the success of harmonization process by assessing the timing, coverage, and performance between the study sites.MethodWe collected data on animal housing conditions, lateral fluid-percussion injury model production, postoperative care, mortality, post-TBI physiological monitoring, timing of blood sampling and quality, MR imaging timing and protocols, and duration of video-electroencephalography (EEG) follow-up using common data elements. Learning effect in harmonization was assessed by comparing procedural accuracy between the early and late stages of the project.ResultsThe animal housing conditions were comparable between the study sites but the postoperative care procedures varied. Impact pressure, duration of apnea, righting reflex, and acute mortality differed between the study sites (p  0.05). Plasma quality was poor in 4% of the samples in UEF, 1% in Monash and 14% in UCLA. More than 97% of the final cohort were MR imaged at all timepoints in all study sites. The timing of imaging did not differ on D2 and D9 (p > 0.05), but varied at D30, 5 months, and ex vivo timepoints (p  0.05). A decrease in acute mortality and increase in plasma quality across time reflected a learning effect in the TBI production and blood sampling protocols.SignificanceOur study is the first demonstration of the feasibility of protocol harmonization for performing powered preclinical multi-center trials for biomarker and therapy discovery of post-traumatic epilepsy.

Abstract Reverse transcription-quantitative PCR (RT-qPCR) is widely used for monitoring viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in wastewater. Various materials, including plasmid DNA, synthetic nucleic acids, PCR amplicons, genomic DNA, and cDNA, are currently used for SARS-CoV-2 quantification by generating standard curves. We assessed three common standards on quantifying SARS-CoV-2 RNA across nine wastewater treatment plants in Finland, as part of the national wastewater surveillance effort. We pairwise compared RT-qPCR results from 148 wastewater samples, using both IDT (#10006625, IDT, USA) and CODEX standards (#SC2-RNAC-1100, CODEX DNA), and 179 samples using both IDT and EURM019 standards (#EURM-019, European Commission, Joint Research Centre) in our assessment. Amongst the tested standards, the CODEX standard consistently yielded more stable results than either the IDT or EURM019 standards. We found that SARS-CoV-2 levels were higher with the IDT standard (4.36 Log10 GC/100 mL) compared to the CODEX standard (4.05 Log10 GC/100 mL). Similarly, quantification using the IDT standard was higher (5.27 Log10 GC/100 mL) than values obtained with the EURM019 (4.81 Log10 GC/100 mL). SARS-CoV-2 RNA quantified with IDT and CODEX standards exhibited stronger concordance (Spearman’s correlation rho median of 0.79) compared to those quantified with IDT and EURM019 standards (rho median of 0.59). This study highlights the significant impact of standard material selection on SARS-CoV-2 RNA quantification, emphasizing the need for harmonization in standard material.

Abstract Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success.

ABSTRACT: The objective of this experiment was to investigate the effect of lipid from rapeseed cake and oats on ruminal CH4 emission and lactational performance of dairy cows. Twelve lactating Nordic Red cows, of which 4 were primiparous, and averaging (±SD) 48 ± 22.9 DIM, 37.8 ± 7.14 kg/d milk yield were enrolled in a switch-back design experiment with 3 periods of 4 wk each. The cows were assigned into 6 pairs based on parity, DIM, milk yield, and BW at the beginning of the experiment. The experimental treatments were (1) rapeseed cake and oats (RSC+O), and (2) rapeseed meal and barley (RSM+B) as the concentrate feeds. Cows in each pair were randomly assigned to 1 of the 2 groups, which received the treatments in 2 different sequences (i.e., group 1 received RSC+O in period 1 and 3, and RSM+B in period 2, whereas group 2 was fed RSM+B in period 1 and 3, and RSC+O in period 2). The diets consisted of a partially mixed ration with grass silage mixed with either oats or barley, according to the treatment sequence, and the rapeseed cake or meal being mixed into a pellet with either oats or barley according to the treatments, and a mineral mix. The pellet was delivered at a fixed amount (i.e., 6 kg/d for multiparous and 5 kg/d for the primiparous cows) from the milking robot. The actual forage to concentrate ratios for RSC+O and RSM+B were 51:49 and 52:48, respectively, with NDF concentrations of 41.5% and 36.0% and CP concentrations of 17.0% and 16.7% of diet DM. Dry matter intake, milk yield, and gas exchange (with a GreenFeed system attached to the milking robot) were recorded daily, and milk composition and spot fecal samples were collected during the last week of each period. Based on feed analysis, and DMI of the cows during the experiment, the total fat content of the experimental diets was 4.1% and 2.7% of DM for RSC+O and RSM+B diets, respectively. Dry matter intake was 1.6 kg/d lower, and milk yield tended to be 1.0 kg/d greater for RSC+O versus RSM+B. There were no differences in ECM yield and milk composition between the treatments, whereas milk ME efficiency was greater for cows fed RSC+O than RSM+B. Methane yield (g/kg DMI) did not differ between treatments, but CH4 production (g/d) was 9.4% and CH4 intensity as g/kg ECM was 11.7% lower for RSC+O versus RSM+B. The lower CH4 production was likely caused by the lower DMI and fiber digestibility, observed with the RSC+O diet. In addition, the greater lipid intake also contributed to lower rate of fermentation and subsequent decrease in CH4 production. Overall, feeding rapeseed cake with oats in a grass silage–based diet increased feed efficiency while decreasing CH4 emission intensity in lactating cows. This provides a practical way of mitigating ruminal CH4 emission from dairy operations while maintaining milk production with commonly used feedstuffs in Nordic conditions.

The rise of evaluation and data in education and education policy is a trend manifesting across a wide variety of policy contexts, holding in its grip national and global policies, with impacts that reach the level of individual children, teachers, and their subjectivities. Earlier research has mainly focused on the phenomenon in neoliberal contexts. This article presents a case study of Finnish early childhood education and care (ECEC) policy discourse built on a tradition of social democratic political rationality and egalitarian values. By utilizing Evert Vedung's metaphor of evaluation waves, we examine how the evaluation trend has influenced the Finnish ECEC policy, which has traditionally been skeptical toward the assessment of individual children's learning and skills and the production and use of data for accountability purposes. In the genealogical reading of ECEC curricula and policy documents from 2002-2021 as data, we identify three discursive practices -- "partnership, pedagogization, and evidence wave" -- that have socio-historically contributed to the formation of the present conception of evaluation in the Finnish ECEC policy discourse. Our genealogical reading also reveals a movement toward international trends -- evaluating the individual child's skills -- especially through the evidence wave.

Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.

The Copernicus Atmosphere Monitoring Service (CAMS) provides daily analyses and forecasts of the composition of the atmosphere, including the reactive gases such as O3, CO, NO2, HCHO and SO2; aerosol species; and greenhouse gases. The global CAMS analysis system (IFS-COMPO) is based on the ECMWF Integrated Forecasting System (IFS) for numerical weather prediction (NWP) and assimilates a large number of composition satellite products on top of the meteorological observations ingested in IFS. The CAMS system receives regular upgrades, following the upgrades of IFS. The last upgrade, Cy48R1, operational since 27 June 2023, was major with a large number of code changes, both for IFS-COMPO and for NWP. The main IFS-COMPO innovations include the introduction of full stratospheric chemistry; a major update of the emissions; a major update of the aerosol model, including the representation of secondary organic aerosol; several updates of the dust life cycle and optics; updates to the inorganic chemistry in the troposphere; and the assimilation of Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD) and TROPOspheric Monitoring Instrument (TROPOMI) CO. The CAMS Cy48R1 upgrade was validated using a large number of independent measurement datasets, including surface in situ, surface remote sensing, routine aircraft, and balloon and satellite observations. In this paper we present the validation results for Cy48R1 by comparing them with the skill of the previous operational system (Cy47R3), with the independent observations as reference, for the period October 2022 to June 2023, during which daily forecasts from both cycles are available. Major improvements in skill are found for the ozone profile in the lower–middle stratosphere and for stratospheric NO2 due to the inclusion of full stratospheric chemistry. Stratospheric trace gases compare well with the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) observations between 10 and 200 hPa, with larger deviations between 1 and 10 hPa. The impact of the updated emissions is especially visible over East Asia and is beneficial for the trace gases O3, NO2 and SO2. The CO column assimilation is now anchored by the Infrared Atmospheric Sounding Interferometer (IASI) instead of the Measurements Of Pollution in The Troposphere (MOPITT) instrument, which is beneficial for most of the CO comparisons, and the assimilation of TROPOMI CO data improves the model CO field in the troposphere. In general the aerosol optical depth has improved globally, but the dust evaluation shows more mixed results. The results of the 47 comparisons are summarised in a scorecard, which shows that 83 % of the evaluation datasets show a neutral or improved performance of Cy48R1 compared to the previous operational CAMS system, while 17 % indicate a (slight) degradation. This demonstrates the overall success of this upgrade.

Background Associations between violent victimisation and psychiatric disorders are hypothesised to be bidirectional, but the role of violent victimisation in the aetiologies of psychiatric disorders and other adverse outcomes remains unclear. We aimed to estimate associations between violent victimisation and subsequent common psychiatric disorders, suicidal behaviours, and premature mortality while accounting for unmeasured familial confounders. Methods and findings Using nationwide registers, we identified a total of 127,628 individuals born in Finland (1987 to 2004) and Sweden (1973 to 2004) who had experienced violent victimisation, defined as either hospital admissions or secondary care outpatient visits for assault-related injuries. These were age- and sex-matched with up to 10 individuals in the general population (n = 1,276,215). Additionally, we matched those who had experienced violent victimisation with their unaffected siblings (n = 132,408). Outcomes included depression, anxiety, personality disorders, alcohol use disorders, drug use disorders, suicidal behaviours, and premature mortality. Participants were followed from the victimisation date until the date of the outcome, emigration, death, or December 31, 2020, whichever occurred first. Country-specific associations were estimated using stratified Cox regression models, which also accounted for unmeasured familial confounders via sibling comparisons. The country-specific associations were then pooled using meta-analytic models. Among 127,628 patients (69.0% male) who had experienced violent victimisation, the median age at first violent victimisation was 21 (interquartile range: 18 to 26) years. Incidence of all outcomes was larger in those who were exposed to violent victimisation compared to population controls, ranging from 2.3 (95% confidence interval (CI) [2.2; 2.4]) per 1,000 person-years for premature mortality (compared with 0.6, 95% CI [0.6; 0.6], in controls) to 22.5 (95% CI [22.3; 22.8]) per 1,000 person-years for anxiety (compared with 7.3, 95% CI [7.3; 7.4], in controls). In adjusted models, people who had experienced violent victimisation were between 2 to 3 times as likely as their siblings to develop any of the outcomes, ranging from adjusted hazard ratio [aHR] 1.7 (95% CI [1.7; 1.8]) for depression to 3.0 (95% CI [2.9; 3.1]) for drug use disorders. Risks remained elevated 2 years post-victimisation, ranging from aHR 1.4 (95% CI [1.3; 1.5]) for depression to 2.3 (95% CI [2.2; 2.4]) for drug use disorders. Our reliance on secondary care data likely excluded individuals with milder assault-related injuries and less severe psychiatric symptoms, thus suggesting that our estimates may be conservative. Another limitation is the possibility of residual genetic confounding, as full siblings share on average about half of their co-segregating genes. However, the associations remained robust even after adjusting for both measured and unmeasured familial confounders. Conclusions In this longitudinal cross-national cohort study, we observed that those who had experienced violent victimisation were at least twice as likely as their unaffected siblings to develop common psychiatric disorders (i.e., depression, anxiety, personality disorder, and alcohol and drug use disorders), engage in suicidal behaviours, and to die prematurely. Importantly, these risk elevations remained 2 years after the first victimisation event. Improving clinical assessment, management, and aftercare psychosocial support could therefore potentially reduce rates of common psychiatric disorders, suicidality, and premature mortality in individuals experiencing violent victimisation., Author(s): Amir Sariaslan 1,*, Joonas Pitkänen 2,3, Jonas Forsman 4,5, Ralf Kuja-Halkola 6, Isabell Brikell 6,7,8, Brian M. D'Onofrio 6,9, Mikko Aaltonen 10,11, Henrik Larsson 6,12, Pekka Martikainen 2,3, Paul [...]

BACKGROUND: Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown. METHODS: We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. In contrast to many previous methods, MuAt utilizes the attention mechanism on individual mutations instead of aggregated mutation counts. RESULTS: We trained MuAt models on 2587 whole cancer genomes (24 tumour types) from the Pan-Cancer Analysis of Whole Genomes (PCAWG) and 7352 cancer exomes (20 types) from the Cancer Genome Atlas (TCGA). MuAt achieved prediction accuracy of 89% for whole genomes and 64% for whole exomes, and a top-5 accuracy of 97% and 90%, respectively. MuAt models were found to be well-calibrated and perform well in three independent whole cancer genome cohorts with 10,361 tumours in total. We show MuAt to be able to learn clinically and biologically relevant tumour entities including acral melanoma, SHH-activated medulloblastoma, SPOP-associated prostate cancer, microsatellite instability, POLE proofreading deficiency, and MUTYH-associated pancreatic endocrine tumours without these tumour subtypes and subgroups being provided as training labels. Finally, scrunity of MuAt attention matrices revealed both ubiquitous and tumour-type specific patterns of simple and complex somatic mutations. CONCLUSIONS: Integrated representations of somatic alterations learnt by MuAt were able to accurately identify histological tumour types and identify tumour entities, with potential to impact precision cancer medicine.

In 2019–2023 the 13th Finnish National Forest Inventory (NFI) was implemented by measuring a total of 62 266 sample plots across the country. The methodology of the sampling and measurements was similar as in the previous inventory, but the proportion and number of remeasured permanent plots was increased to improve the monitoring of annual increment and other changes in the forests. Only 6.2 M ha (14%) of Finland’s total land area (30.4 M ha) is other land than forestry land. Productive and poorly productive forests cover 22.9 M ha (75%) of the total land area. The forest area has remained stable in recent decades but the forest area available for wood supply (FAWS) has decreased due to increased forest protection – 23% of the forestry land and 10% of the productive forest are not available for wood supply. Compared to the previous inventory, forest resources have continued to increase but the average annual increment has declined from 107.8 M m3 to 103.0 M m3. The quality of forests from the timber production point of view has remained relatively good or improved slightly. The area of observed forest damage on FAWS is 8.4 M ha (46% of FAWS area), half of these minor damages with no impact on stand quality. Although the area of forest damage has not increased, the amount of mortality has continued to increase, and is now 8.8 M m3 year–1. The amount of dead wood has continued to increase in South Finland, while in North Finland the declining trend has turned into a slight increase. Since the 1920s, the area of forestry land has remained stable, but the area of productive forest has increased due to the drainage of poorly productive or treeless peatlands. The total volume of growing stock has increased by 84% and annual increment has more than doubled.

Orchestrated action of peptidoglycan (PG) synthetases and hydrolases is vital for bacterial growth and viability. Although the function of several PG synthetases and hydrolases is well understood, the function, regulation, and mechanism of action of PG hydrolases characterised as lysostaphin-like endopeptidases have remained elusive. Many of these M23 family members can hydrolyse glycyl-glycine peptide bonds and show lytic activity against Staphylococcus aureus whose PG contains a pentaglycine bridge, but their exact substrate specificity and hydrolysed bonds are still vaguely determined. In this work, we have employed NMR spectroscopy to study both the substrate specificity and the bond cleavage of the bactericide lysostaphin and the S. aureus PG hydrolase LytM. Yet, we provide substrate-level evidence for the functional role of these enzymes. Indeed, our results show that the substrate specificities of these structurally highly homologous enzymes are similar, but unlike observed earlier both LytM and lysostaphin prefer the D-Ala-Gly cross-linked part of mature peptidoglycan. However, we show that while lysostaphin is genuinely a glycyl-glycine hydrolase, LytM can also act as a D-alanyl-glycine endopeptidase.

Wastewater-based surveillance (WBS) of infectious disease agents is increasingly seen as a reliable source of population health data. To date, wastewater-based surveillance efforts have largely focused on individual pathogens. However, given that wastewater contains a broad range of pathogens circulating in the population, a more comprehensive approach could enhance its usability. We focused on the simultaneous detection of SARS-CoV-2, sapovirus, Campylobacter jejuni, Campylobacter coli, Salmonella spp., pathogenic Escherichia coli, Cryptosporidium spp., Giardia spp. and antimicrobial resistance genes (ARGs) of clinical relevance. To achieve this goal, biomass concentration and nucleic acid extraction methods were optimized, and samples were analyzed by using a set of (RT)-qPCR and (HT)-qPCR methods. We determined the prevalence and the spatial and temporal trends of the targeted pathogens and collected novel information on ARGs in Finnish wastewater. In addition, the use of different wastewater concentrates, namely the ultrafiltered concentrate of the supernatant and the centrifuged pellet, and the effect of freezing and thawing wastewater prior to sample processing were investigated with the indicator microbe crAssphage. Freeze-thawing of wastewater decreased the gene copy count of crAssphage in comparison to analyzing fresh samples (p

Introduction A better understanding of the earliest stages of Alzheimer’s disease (AD) could expedite the development or administration of treatments. Large population biobanks hold the promise to identify individuals at an elevated risk of AD and related dementias based on health registry information. Here, we establish the protocol for an observational clinical recall and biomarker study called TWINGEN with the aim to identify individuals at high risk of AD by assessing cognition, health and AD-related biomarkers. Suitable candidates were identified and invited to participate in the new study among THL Biobank donors according to TWINGEN study criteria.Methods and analysis A multi-centre study (n=800) to obtain blood-based biomarkers, telephone-administered and web-based memory and cognitive parameters, questionnaire information on lifestyle, health and psychological factors, and accelerometer data for measures of physical activity, sedentary behaviour and sleep. A subcohort is being asked to participate in an in-person neuropsychological assessment (n=200) and wear an Oura ring (n=50). All participants in the TWINGEN study have genome-wide genotyping data and up to 48 years of follow-up data from the population-based older Finnish Twin Cohort (FTC) study of the University of Helsinki. The data collected in TWINGEN will be returned to THL Biobank from where it can later be requested for other biobank studies such as FinnGen that supported TWINGEN.Ethics and dissemination This recall study consists of FTC/THL Biobank/FinnGen participants whose data were acquired in accordance with the Finnish Biobank Act. The recruitment protocols followed the biobank protocols approved by Finnish Medicines Agency. The TWINGEN study plan was approved by the Ethics Committee of Hospital District of Helsinki and Uusimaa (number 16831/2022). THL Biobank approved the research plan with the permission no: THLBB2022_83.

Abstract 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.

Abstract Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants may have different characteristics, e.g., in transmission, mortality, and the effectiveness of vaccines, indicating the importance of variant detection at the population level. Wastewater-based surveillance of SARS-CoV-2 RNA fragments has been shown to be an effective way to monitor the COVID-19 pandemic at the population level. Wastewater is a complex sample matrix affected by environmental factors and PCR inhibitors, causing insufficient coverage in sequencing, for example. Subsequently, results where part of the genome does not have sufficient coverage are not uncommon. To identify variants and their proportions in wastewater over time, we utilized next-generation sequencing with the ARTIC Network's primer set and bioinformatics pipeline to evaluate the presence of variants in partial genome data. Based on the wastewater data from November 2021 to February 2022, the Delta variant was dominant until mid-December in Helsinki, Finland’s capital, and thereafter in late December 2022 Omicron became the most common variant. At the same time, the Omicron variant of SARS-CoV-2 outcompeted the previous Delta variant in Finland in new COVID-19 cases. The SARS-CoV-2 variant findings from wastewater are in agreement with the variant information obtained from the patient samples when visually comparing trends in the sewerage network area. This indicates that the sequencing of wastewater is an effective way to monitor temporal and spatial trends of SARS-CoV-2 variants at the population level.

Wastewater-based surveillance (WBS) of multidrug-resistant bacteria could complement clinical data, serving as a population-level early warning tool. This study evaluated WBS as a pandemic preparedness tool, by selectively isolating and culturing methicillin-resistant Staphylococcus aureus (MRSA) with CHROMagar MRSA. Some 24-h composite wastewater samples (n = 80) were collected from ten treatment plants across Finland between February 2021 and January 2022. MRSA prevalence in wastewater samples was 27.5% (n = 22/80), showing seasonal and temporal variations. Phenotypic antimicrobial susceptibility testing (AST) with microdilution showed that over 80% of isolates were drug-resistant to clindamycin, sulfamethoxazole/trimethoprim, tetracycline, fusidic acid, and erythromycin. Four isolates (18.2%) were vancomycin-resistant. WGS revealed that 31.8% (n = 7) of the isolates belonged to the ST8-t008 and ST6-t304 spa types, respectively. In addition, two spa types (t011 and t034) belong to the CC398 complex. The mecA gene was found in all isolates (n = 22) and three tetracycline resistance determinants (tet38, tetK, and tetM) were detected with tet38 being the most abundant (81.8%, n = 18/22). Three isolates harboured the plasmid-mediated sat4 gene that confers resistance to Streptothricin. In addition, resistance determinants to macrolide antibiotics (mph (C)/msr (A) and fosfomycin (fosB) were detected in the seven isolates that belonged to spa type t008. All isolates except one harboured the SCCmec_type_IVa(2B). Six ST8 isolates harboured the LukS/F-PV genes encoding the Panton–Valentine leukocidin (PVL) and were also positive for the Arginine Catabolic Mobile Element (ACME), suggesting they belong to the USA300 clone. The Inc18 plasmid was the most abundant as it was detected in 72.7% (n = 16/22) of the isolates. Other plasmid replicons detected were the rep_trans and repA_N which were detected in 45.4% (n = 10/22) and 40.9% (n = 9/22) of the isolates respectively. Ten isolates harboured at least three plasmid replicons and no plasmid replicons were detected in four isolates (ST6/t304). The cgMLST revealed that some isolates aggregated into two genomically indistinguishable clusters: ST6/t304 belonging to cluster type CT12405 (≤20 allelic differences) and ST8/t008 belonging to cluster type CT1925 (

With more than 6000 attendees between in-person and virtual offerings, the American Epilepsy Society Meeting 2022 in Nashville, felt as busy as in prepandemic times. An ever-growing number of physicians, scientists, and allied health professionals gathered to learn a variety of topics about epilepsy. The program was carefully tailored to meet the needs of professionals with different interests and career stages. This article summarizes the different symposia presented at the meeting. Basic science lectures addressed the primary elements of seizure generation and pathophysiology of epilepsy in different disease states. Scientists congregated to learn about anti-seizure medications, mechanisms of action, and new tools to treat epilepsy including surgery and neurostimulation. Some symposia were also dedicated to discuss epilepsy comorbidities and practical issues regarding epilepsy care. An increasing number of patient advocates discussing their stories were intertwined within scientific activities. Many smaller group sessions targeted more specific topics to encourage member participation, including Special Interest Groups, Investigator, and Skills Workshops. Special lectures included the renown Hoyer and Lombroso, an ILAE/IBE joint session, a spotlight on the impact of Dobbs v. Jackson on reproductive health in epilepsy, and a joint session with the NAEC on coding and reimbursement policies. The hot topics symposium was focused on traumatic brain injury and post-traumatic epilepsy. A balanced collaboration with the industry allowed presentations of the latest pharmaceutical and engineering advances in satellite symposia.

BackgroundMany countries have applied the wastewater surveillance of the COVID-19 pandemic to their national public health monitoring measures. The most used methods for detecting SARS-CoV-2 in wastewater are quantitative reverse transcriptase–polymerase chain reaction (RT-qPCR) and reverse transcriptase–droplet digital polymerase chain reaction (RT-ddPCR). Previous comparison studies have produced conflicting results, thus more research on the subject is required. ObjectiveThis study aims to compare RT-qPCR and RT-ddPCR for detecting SARS-CoV-2 in wastewater. It also aimed to investigate the effect of changes in the analytical pipeline, including the RNA extraction kit, RT-PCR kit, and target gene assay, on the results. Another aim was to find a detection method for low-resource settings. MethodsWe compared 2 RT-qPCR kits, TaqMan RT-qPCR and QuantiTect RT-qPCR, and RT-ddPCR based on sensitivity, positivity rates, variability, and correlation of SARS-CoV-2 gene copy numbers in wastewater to the incidence of COVID-19. Furthermore, we compared 2 RNA extraction methods, column- and magnetic-bead–based. In addition, we assessed 2 target gene assays for RT-qPCR, N1 and N2, and 2 target gene assays for ddPCR N1 and E. Reverse transcription strand invasion-based amplification (RT-SIBA) was used to detect SARS-CoV-2 from wastewater qualitatively. ResultsOur results indicated that the most sensitive method to detect SARS-CoV-2 in wastewater was RT-ddPCR. It had the highest positivity rate (26/30), and its limit of detection was the lowest (0.06 gene copies/µL). However, we obtained the best correlation between COVID-19 incidence and SARS-CoV-2 gene copy number in wastewater using TaqMan RT-qPCR (correlation coefficient [CC]=0.697, P

Background: Neurotrophic tyrosine receptor kinase (NTRK) gene fusions are oncogenic drivers. Using the Auria Biobank in Finland, we aimed to identify and characterize patients with these gene fusions, and describe their clinical and tumor characteristics, treatments received, and outcomes. Material and methods: We evaluated pediatrics with any solid tumor type and adults with colorectal cancer (CRC), non-small cell lung cancer (NSCLC), sarcoma, or salivary gland cancer. We determined tropomyosin receptor kinase (TRK) protein expression by pan-TRK immunohistochemistry (IHC) staining of tumor samples from the Auria Biobank, scored by a certified pathologist. NTRK gene fusion was confirmed by next generation sequencing (NGS). All 2,059 patients were followed-up starting 1 year before their cancer diagnosis. Results: Frequency of NTRK gene fusion tumors was 3.1% (4/127) in pediatrics, 0.7% (8/1,151) for CRC, 0.3% (1/288) for NSCLC, 0.9% (1/114) for salivary gland cancer, and 0% (0/379) for sarcoma. Among pediatrics there was one case each of fibrosarcoma (TPM3::NTRK1), Ewing’s sarcoma (LPPR1::NTRK2), primitive neuroectodermal tumor (DAB2IP::NTRK2), and papillary thyroid carcinoma (RAD51B::NTRK3). Among CRC patients, six harbored tumors with NTRK1 fusions (three fused with TPM3), one harbored a NTRK3::GABRG1 fusion, and the other a NTRK2::FXN/LPPR1 fusion. Microsatellite instability was higher in CRC patients with NTRK gene fusion tumors versus wild-type tumors (50.0% vs. 4.4%). Other detected fusions were SGCZ::NTRK3 (NSCLC) and ETV6::NTRK3 (salivary gland cancer). Four patients (three CRC, one NSCLC) received chemotherapy; one patient (with CRC) received radiotherapy. Conclusion: NTRK gene fusions are rare in adult CRC, NSCLC, salivary tumors, sarcoma, and pediatric solid tumors.