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Estimates of PM2.5 levels are crucial for monitoring air quality and studying the epidemiological impact of air quality on the population. Currently, the most precise measurements of PM2.5 are obtained from ground stations, resulting in limited spatial coverage. In this study, we consider satellite-based PM2.5 retrieval, which involves conversion of high-resolution satellite retrieval of aerosol optical depth (AOD) into high-resolution PM2.5 retrieval. To improve the accuracy of the AOD-to-PM2.5 conversion, we employ the machine-learning-based post-process correction to correct the AOD-to-PM conversion ratio derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis model data. The post-process-correction approach utilizes a fusion and downscaling of satellite observation and retrieval data, MERRA-2 reanalysis data, various high-resolution geographical indicators, meteorological data, and ground station observations for learning a predictor for the approximation error in the AOD-to-PM2.5 conversion ratio. The corrected conversion ratio is then applied to estimate PM2.5 levels given the high-resolution satellite AOD retrieval data derived from Sentinel-3 observations. The region of study is central Europe during the year 2019. Our model produces PM2.5 estimates with a spatial resolution of 100 m at satellite overpass times with R2 = 0.55 and RMSE = 6.2 µg m−3. The corresponding metrics for monthly averages are R2 = 0.72 and RMSE = 3.7 µg m−3. Additionally, we have incorporated an ensemble of neural networks to provide error envelopes for machine-learning-related uncertainty in the PM2.5 estimates. The proposed approach can produce accurate high-resolution PM2.5 data that can be very useful for air quality monitoring, emission regulation, and epidemiological studies.

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

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

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 (

IntroductionEstrogen deficiency is associated with unfavorable changes in body composition and metabolic health. While physical activity ameliorates several of the negative effects, loss of ovarian function is associated with decreased physical activity levels. It has been proposed that the changes in brain neurochemical levels and /or impaired skeletal muscle function may underlie this phenomenon.MethodsWe studied the effect of estrogen deficiency induced via ovariectomy (OVX) in female Wistar rats (n = 64). Rats underwent either sham or OVX surgery and were allocated thereafter into four groups matched for body mass and maximal running capacity: sham/control, sham/max, OVX/control, and OVX/max, of which the max groups had maximal running test before euthanasia to induce acute response to exercise. Metabolism, spontaneous activity, and maximal running capacity were measured before (PRE) and after (POST) the surgeries. Three months following the surgery, rats were euthanized, and blood and tissue samples harvested. Proteins were analyzed from gastrocnemius muscle and retroperitoneal adipose tissue via Western blot. Brain neurochemical markers were measured from nucleus accumbens (NA) and hippocampus (HC) using ultra-high performance liquid chromatography.ResultsOVX had lower basal energy expenditure and higher body mass and retroperitoneal adipose tissue mass compared with sham group (p ≤ 0.005). OVX reduced maximal running capacity by 17% (p = 0.005) with no changes in muscle mass or phosphorylated form of regulatory light chain (pRLC) in gastrocnemius muscle. OVX was associated with lower serotonin metabolite 5-hydroxyindoleacetic acid (5-HIAA) level in the NA compared with sham (p = 0.007). In response to acute exercise, OVX was associated with low serotonin level in the HC and high level in the NA (p ≤ 0.024).DiscussionOur results highlight that OVX reduces maximal running capacity and affects the response of brain neurochemical levels to acute exercise in a brain region-specific manner. These results may offer mechanistic insight into why OVX reduces willingness to exercise.

Abstract Background Antimicrobial resistance (AMR) is a critical threat to human health. Escherichia coli and Klebsiella pneumoniae are clinically the most important species associated with AMR and are the most common carbapenemase-producing (CP) Enterobacterales detected in human specimens in Finland. Wastewater surveillance has emerged as a potential approach for population-level surveillance of AMR, as wastewater could offer a reflection from a larger population with one sample and minimal recognized ethical issues. In this study, we investigated the potential of wastewater surveillance to detect CP E. coli and K. pneumoniae strains similar to those detected in human specimens. Methods Altogether, 89 composite samples of untreated community wastewater were collected from 10 wastewater treatment plants across Finland in 2021–2022. CP E. coli and K. pneumoniae were isolated using selective culture media and identified using MALDI-TOF MS. Antimicrobial susceptibility testing was performed using disk diffusion test and broth microdilution method, and a subset of isolates was characterized using whole-genome sequencing. Results CP E. coli was detected in 26 (29.2%) and K. pneumoniae in 25 (28.1%) samples. Among E. coli, the most common sequence type (ST) was ST410 (n = 7/26, 26.9%), while ST359 (n = 4/25, 16.0%) predominated among K. pneumoniae. Globally successful STs were detected in both E. coli (ST410, ST1284, ST167, and ST405) and K. pneumoniae (ST512, ST101, and ST307). K. pneumoniae carbapenemases (KPC) were the most common carbapenemases in both E. coli (n = 11/26, 42.3%) and K. pneumoniae (n = 13/25, 52.0%), yet also other carbapenemases, such as bla NDM-5, bla OXA-48, and bla OXA-181, were detected. We detected isolates harboring similar ST and enzyme type combinations previously linked to clusters in Finland, such as E. coli ST410 with bla KPC-2 and K. pneumoniae ST512 with bla KPC-3. Conclusions Our study highlights the presence of clinically relevant strains of CP E. coli and K. pneumoniae in community wastewater. The results indicate that wastewater surveillance could serve as a monitoring tool for CP Enterobacterales. However, the specificity and sensitivity of the methods should be improved, and technologies, like advanced sequencing methods, should be utilized to distinguish data with public health relevance, harness the full potential of wastewater surveillance, and implement the data in public health surveillance.

In May–June 2019, smoke plumes from wildfires in Alberta, Canada, were advected all the way to Europe. To analyze the evolution of the plumes and to estimate the amount of smoke aerosols transported to Europe, retrievals from the spaceborne lidar CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) were used. The plumes were located with the help of a trajectory analysis, and the masses of smoke aerosols were retrieved from the CALIOP observations. The accuracy of the CALIOP mass retrievals was compared with the accuracy of ground-based lidars/ceilometer near the source in North America and after the long-range transport in Europe. Overall, CALIOP and the ground-based lidars/ceilometer produced comparable results. Over North America the CALIOP layer mean mass was 30 % smaller than the ground-based estimates, whereas over southern Europe that difference varied between 12 % and 43 %. Finally, the CALIOP mass retrievals were compared with simulated aerosol concentrations from two reanalysis models: MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) and CAMS (Copernicus Atmospheric Monitoring System). The simulated total column aerosol optical depths (AODs) and the total column mass concentration of smoke agreed quite well with CALIOP observations, but the comparison of the layer mass concentration of smoke showed significant discrepancies. The amount of smoke aerosols in the model simulations was consistently smaller than in the CALIOP retrievals. These results highlight the limitations of such models and more specifically their limitation to reproduce properly the smoke vertical distribution. They indicate that CALIOP is a useful tool monitoring smoke plumes over secluded areas, whereas reanalysis models have difficulties in representing the aerosol mass in these plumes. This study shows the advantages of spaceborne aerosol lidars, e.g., being of paramount importance to monitor smoke plumes, and reveals the urgent need of future lidar missions in space.

Here we present for the first time a proof of concept for an emulation-based method that uses a large-eddy simulations (LESs) to present sub-grid cloud processes in a general circulation model (GCM). We focus on two key variables affecting the properties of shallow marine clouds: updraft velocity and precipitation formation. The LES is able to describe these processes with high resolution accounting for the realistic variability in cloud properties. We show that the selected emulation method is able to represent the LES outcome with relatively good accuracy and that the updraft velocity and precipitation emulators can be coupled with the GCM practically without increasing the computational costs. We also show that the emulators influence the climate simulated by the GCM but do not consistently improve or worsen the agreement with observations on cloud-related properties, although especially the updraft velocity at cloud base is better captured. A more quantitative evaluation of the emulator impacts against observations would, however, have required model re-tuning, which is a significant task and thus could not be included in this proof-of-concept study. All in all, the approach introduced here is a promising candidate for representing detailed cloud- and aerosol-related sub-grid processes in GCMs. Further development work together with increasing computing capacity can be expected to improve the accuracy and the applicability of the approach in climate simulations.

The literature widely presents methods that allow evaluating and comparing the decorative qualities of woody plants of different genera and families. But there is no universal method for assessing the decorative qualities of woody plants and the existing methods are not applicable to high latitudes. The purpose of the work was to develop approaches and methods for assessing the decorativeness of woody plants in the Far North. The following approaches were used: the exclusion of the descriptive characteristics of the criterion, the introduction of a question-answer (yes or no) system, the introduction of measurable criteria; accounting of at least 3–5-year data of phenological observations. The assessment of the decorative qualities of woody plants consists of evaluating the crown architectonics, foliage, flowering and fruiting. It includes reduction coefficients that take into account the amount of plant organ damage. The overall assessment of the object can be represented by the sum of all criteria assessments or the decorativeness curve. The advantages of the proposed methodology are the exclusion of the descriptive characteristics of the criterion and the use of criteria calculated on the basis of real data on the plant, while providing the possibility of refining the coefficients for other places of growth.

Varhaiskasvatuksen aloitus ja siihen liittyvä ero huoltajasta herättävät toisinaan pienissä lapsissa surua. Tässä tutkimuksessa tarkastelemme varhaiskasvatuksen aloitusta vuorohoidon kontekstissa ja selvitämme, millaista pienen lapsen suru ja sen kohtaaminen on vaihtelevassa sosiaalisessa ympäristössä. Ymmärrämme surun kulttuuripsykologisena, vuorovaikutuksessa rakentuvana ilmiönä. Aineistonamme on ympärivuorokautisen päiväkodin alle 3-vuotiaiden lasten ryhmissä kerätty havainnointimateriaali, josta keskitymme analysoimaan kolmen 9–16 kk ikäisen lapsen ensimmäisiä kuukausia varhaiskasvatuksessa. Tutkimuksemme perusteella niin lapsesta huolehtivien aikuisten vaihtuminen kuin vertaisryhmässä tapahtuvat muutokset ovat lapsen surun ja sen kohtaamisen kannalta merkityksellisiä ja voivat yhtäältä voimistaa lapsen surua, toisaalta tyynnyttää surevaa lasta ja luoda mahdollisuuksia surun myötätuntoiseen kohtaamiseen. Suuressa aikuisten tiimissä lapsen surun kohtaamiseen liittyvä yhteistyö ja tiedon jakaminen korostuu. Huomioimalla vuorohoidon erityispiirteet tutkimus laajentaa varhaiskasvatuksen aloitukseen liittyvän surun ja sen kohtaamisen ymmärrystä.

This article presents a method within a Bayesian framework for quantifying uncertainty in satellite aerosol remote sensing when retrieving aerosol optical depth (AOD). By using a Bayesian model averaging technique, we take into account uncertainty in aerosol optical model selection and also obtain a shared inference about AOD based on the best-fitting optical models. In particular, uncertainty caused by forward-model approximations has been taken into account in the AOD retrieval process to obtain a more realistic uncertainty estimate. We evaluated a model discrepancy, i.e., forward-model uncertainty, empirically by exploiting the residuals of model fits and using a Gaussian process to characterise the discrepancy. We illustrate the method with examples using observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite. We evaluated the results against ground-based remote sensing aerosol data from the Aerosol Robotic Network (AERONET).

Abstract Systemic inflammation triggers protective as well as pro-inflammatory responses in the brain based on neuronal and/or cytokine signaling, and it associates with acutely and protractedly disrupted cognition. However, the multiple mechanisms underlying the peripheral–central inflammatory signaling are still not fully characterized. We used intraperitoneal (i.p.) injection of lipopolysaccharide (LPS) in freely moving mice with chronically implanted electrodes for recording of local field potentials (LFP) and electrocorticography (ECoG) in the hippocampus and neocortex, respectively. We show here that a sudden switch in the mode of network activity occurred in both areas starting at 10–15 min after the LPS injection, simultaneously with a robust change from exploration to sickness behavior. This switch in cortical mode commenced before any elevations in pro-inflammatory cytokines IL-1β, TNFα, CCL2 or IL-6 were detected in brain tissue. Thereafter, this mode dominated cortical activity for the recording period of 3 h, except for a partial and transient recovery around 40 min post-LPS. These effects were closely paralleled by changes in ECoG spectral entropy. Continuous recordings for up to 72 h showed a protracted attenuation in hippocampal activity, while neocortical activity recovered after 48 h. The acute sickness behavior recovered by 72 h post-LPS. Notably, urethane (1.3 mg/kg) administered prior to LPS blocked the early effect of LPS on cortical activity. However, experiments under urethane anesthesia which were started 24 h post-LPS (with neuroinflammation fully developed before application of urethane) showed that both theta–supratheta and fast gamma CA1 activity were reduced, DG delta activity was increased, and sharp-wave ripples were abolished. Finally, we observed that experimental compensation of inflammation-induced hypothermia 24–48 h post-LPS promoted seizures and status epilepticus; and that LPS decreased the threshold of kainate-provoked seizures beyond the duration of acute sickness behavior indicating post-acute inflammatory hyperexcitability. Taken together, the strikingly fast development and initial independence of brain cytokines of the LPS-induced cortical mode, its spectral characteristics and simultaneity in hippocampus and neocortex, as well as inhibition by pre-applied urethane, strongly suggest that the underlying mechanisms are based on activation of the afferent vagus nerve and its mainly cholinergic ascending projections to higher brain areas.

Abstract Background Obesity is a global issue with a major impact on cardiovascular health. This study explores how obesity influences nocturnal cardiac electrophysiology in suspected obstructive sleep apnea (OSA) patients. Methods We randomly selected 12 patients from each of the five World Health Organization body mass index (BMI) classifications groups (ntotal = 60) while keeping the group's age and sex matched. We evaluated 1965 nocturnal electrocardiography (ECG) samples (10 s) using modified lead II recorded during normal saturation conditions. R‐wave peaks were detected and confirmed using dedicated software, with the exclusion of ventricular extrasystoles and artifacts. The duration of waves and intervals was manually marked. The average electric potential graphs were computed for each segment. Thresholds for abnormal ECG waveforms were P‐wave > 120 ms, PQ interval > 200 ms, QRS complex > 120 ms for, and QTc > 440 ms. Results Obesity was significantly (p

We studied the potential of using machine learning to downscale global-scale climate model output towards ground station data. The aim was to simultaneously analyze both city-level air quality and regional- and global-scale radiative forcing values for anthropogenic aerosols. As the city-level air pollution values are typically underestimated in global-scale models, we used a machine learning approach to downscale fine particulate (PM2.5) concentrations towards measured values. We first simulated the global climate with the aerosol–climate model ECHAM-HAMMOZ and corrected the PM2.5 values for the Indian megacity New Delhi. The downscaling procedure clearly improved the seasonal variation in the model data. The seasonal trends were much better captured in the corrected PM2.5 than in original ECHAM-HAMMOZ PM2.5 when compared to the reference PM2.5 from the ground stations. However, short-term variations showed less extreme values with the downscaling approach. We applied the downscaling model also to simulations where the aerosol emissions were following two different future scenarios: one following the current legislation and one assuming currently maximum feasible emission reductions. The corrected PM2.5 concentrations for the year 2030 showed that mitigating anthropogenic aerosols improves local air quality in New Delhi, with organic carbon reductions contributing most to these improvements. In addition, aerosol emission mitigation also resulted in negative radiative forcing values over most of India. This was mainly due to reductions in absorbing black carbon emissions. For the two future emission scenarios modeled, the radiative forcing due to aerosol–radiation interactions over India was -0.09±0.26 and -0.53±0.31 W m−2, respectively, while the effective radiative forcing values were -2.1±4.6 and 0.06±3.39 W m−2, respectively. Although accompanied by relatively large uncertainties, the obtained results indicate that aerosol mitigation could bring a double benefit in India: better air quality and decreased warming of the local climate. Our results demonstrate that downscaling and bias correction allow more versatile utilization of global-scale climate models. With the help of downscaling, global climate models can be used in applications where one aims to analyze both global and regional effects of policies related to mitigating anthropogenic emissions.

The authors showed that previous analyses which have estimated that the cloud water content decreases with increasing number of cloud droplets may have a negative bias due to variability in satellite data, thus underestimating aerosol-cloud-climate cooling.

During the COVID-19 lockdown, the dramatic reduction of anthropogenic emissions provided a unique opportunity to investigate the effects of reduced anthropogenic activity and primary emissions on atmospheric chemical processes and the consequent formation of secondary pollutants. Here, we utilize comprehensive observations to examine the response of atmospheric new particle formation (NPF) to the changes in the atmospheric chemical cocktail. We find that the main clustering process was unaffected by the drastically reduced traffic emissions, and the formation rate of 1.5 nm particles remained unaltered. However, particle survival probability was enhanced due to an increased particle growth rate (GR) during the lockdown period, explaining the enhanced NPF activity in earlier studies. For GR at 1.5–3 nm, sulfuric acid (SA) was the main contributor at high temperatures, whilst there were unaccounted contributing vapors at low temperatures. For GR at 3–7 and 7–15 nm, oxygenated organic molecules (OOMs) played a major role. Surprisingly, OOM composition and volatility were insensitive to the large change of atmospheric NOx concentration; instead the associated high particle growth rates and high OOM concentration during the lockdown period were mostly caused by the enhanced atmospheric oxidative capacity. Overall, our findings suggest a limited role of traffic emissions in NPF.

Over the past four decades, Arctic Amplification - the ratio of Arctic to global warming - has been much stronger than thought, and is probably underestimated in climate models, suggest analyses of observations and the CMIP5 and CMIP6 simulations.

Multi-drug resistance is emerging in Citrobacter freundii, which is the third most common carbapenemase-producing (CP) Enterobacteriaceae in humans in Finland due to recent outbreaks. The objective of this study was to determine if wastewater surveillance (WWS) could detect CP C. freundii strains causing infections in humans. Selective culturing was used to isolate CP C. freundii from the hospital environment, hospital wastewater, and untreated municipal wastewater in Helsinki, Finland, between 2019 and 2022. Species were identified using MALDI-TOF, and presumptive CP C. freundii isolates were subjected to antimicrobial susceptibility testing and further characterized by whole genome sequencing. A genomic comparison was conducted to compare isolates collected from the hospital environment, untreated municipal wastewater, and a selection of isolates from human specimens from two hospitals in the same city. We also examined the persistence of CP C. freundii in the hospital environment and the impact of our attempts to eradicate it. Overall, 27 blaKPC − 2-carrying C. freundii were detected in the hospital environment (ST18; n = 23 and ST8; n = 4), while 13 blaKPC − 2-carrying C. freundii (ST8) and five blaVIM − 1-carrying (ST421) C. freundii were identified in untreated municipal wastewater. CP C. freundii was not identified in hospital wastewater. We found three clusters (cluster distance threshold ≤ 10 allelic difference) after comparing the recovered isolates and a selection of isolates from human specimens. The first cluster consisted of ST18 isolates from the hospital environment (n = 23) and human specimens (n = 4), the second consisted of ST8 isolates from the hospital environment (n = 4), untreated municipal wastewater (n = 6), and human specimens (n = 2), and the third consisted of ST421 isolates from the untreated municipal wastewater (n = 5). Our results support previous studies suggesting that the hospital environment could act as a source of transmission of CP C. freundii in clinical settings. Furthermore, the eradication of CP Enterobacteriaceae from the hospital environment is challenging. Our findings also showed that CP C. freundii is persistent throughout the sewerage system and demonstrate the potential of WWS for detecting CP C. freundii.

The early childhood years are pivotal as they mark the beginning of a young child's life journey into education. This paper offers critical reflections of the early childhood care and education landscape in Singapore as it has evolved over the last decade. The discussion will draw on findings of the study "Vital Voices for Vital Years 2" (2019) to explicate the issues, debates and challenges facing the early childhood care and education sector. It argues that recent developments in the sector with stepped increases in government funding and strategic policy development, augmented by the establishment of national agencies committed to improving the quality of care and education, have achieved significant milestones in the country. However, a more critical perspective of the role of early childhood in policy and practice to meet the diverse needs of young children and families is necessary for envisioning education as a pathway to inclusion and social equality, and for building a truly inclusive society.

The composition of organic aerosol under different ambient conditions as well as their phase state have been a subject of intense study in recent years. One way to study particle properties is to measure the particle size shrinkage in a diluted environment at isothermal conditions. From these measurements it is possible to separate the fraction of low-volatility compounds from high-volatility compounds. In this work, we analyse and evaluate a method for obtaining particle composition and viscosity from measurements using process models coupled with input optimization algorithms. Two optimization methods, the Monte Carlo genetic algorithm and Bayesian inference, are used together with process models describing the dynamics of particle evaporation. The process model optimization scheme in inferring particle composition in a volatility-basis-set sense and composition-dependent particle viscosity is tested with artificially generated data sets and real experimental data. Optimizing model input so that the output matches these data yields a good match for the estimated quantities. Both optimization methods give equally good results when they are used to estimate particle composition to artificially test data. The timescale of the experiments and the initial particle size are found to be important in defining the range of values that can be identified for the properties from the optimization.

Abstract Background Obstructive sleep apnea (OSA) is associated with vascular diseases from which stroke and sudden cardiac death are the most significant ones. It is known that disturbances of the autonomic nervous system and electrocardiographic changes are seen in patients with a previous cerebrovascular event. However, the pathophysiological cascade between breathing cessations, autonomic regulation, and cardiovascular events is not fully understood. Methods We aimed to investigate the acute effect of desaturation on repolarisation in OSA patients with a previous stroke. We retrospectively analysed heart-rate corrected QT (QTc) intervals before, within, and after 975 desaturations in OSA patients with a stroke history and at least moderate sleep apnea (apnea–hypopnea index ≥ 15 events/h, n = 18). For the control population (n = 18), QTc intervals related to 1070 desaturation were analysed. Desaturations were assigned to groups according to their length and duration. Groupwise comparisons and regression analyses were further executed to investigate the influence of desaturation features on repolarization. Results In the stroke population the QTc prolonged at least 11 ms during 27.1% of desaturations, and over 20 ms during 12.2% of desaturations. QTc was significantly prolonged during longer (> 30 s, p 7%, p 45 s desaturations and 7.4 ms during > 9% deep desaturations. In the control population, QTc prolongation was observed but to a significantly lesser extent than in stroke patients. In addition, desaturation duration was found to be an independent predictor of QTc prolongation (β = 0.08, p 30 s) and deeper (> 7%) desaturations prolong QTc in patients with stroke history. A significant proportion of desaturations produced clinically relevant QTc prolongation. As it is known that a long QTc interval is associated with lethal arrhythmias, this finding might in part explain the pathophysiological sequelae of cardiovascular mortality in OSA patients with a history of stroke.

The number of cloud droplets formed at the cloud base depends on both the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large-eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parameterisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian process emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produces on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.

Maija Lipponen,1 Ville Hallikainen,2 Pekka Kilpeläinen3 1Unit of Bioeconomy and Environment, Natural Resources Institute Finland, Paltamo, Finland; 2Unit of Natural Resources, Natural Resources Institute Finland, Rovaniemi, Finland; 3Unit of Measurement Technology, Kajaani University Consortium, University of Oulu, Kajaani, FinlandCorrespondence: Maija Lipponen, Unit of Bioeconomy and Environment, Natural Resources Institute Finland, Manamansalontie 90, Paltamo, FI-88300, Finland, Tel +35 8295322024, Email maija.lipponen@luke.fiPurpose: To assess methodology and its limitations for measuring effects of nature-based intervention (NBI).Patients and Methods: Participants were 11 middle-aged female health care workers with lowered capacity to work. NBI included six group appointments in six months study period. Heart rate variability (HRV) and self-reported pain and work exhaustion were measured pre-post study period. Salivary α-amylase samples were collected immediately before and after three individual interventions. Salivary cortisol samples were collected on the same three interventions, on three consecutive days starting from the day of intervention, to assess (a) month effect (pre-post study period) and (b) day effect (intervention day vs non-intervention day).Results: Individual interventions resulted in increase in α-amylase activity. However, the average fold increase decreased from the 3.05 ± 1.20 of the first intervention to 1.91 ± 1.00 and 1.46 ± 0.77 in the second and third intervention, respectively (p < 0.001). Cortisol concentrations were lower on intervention days vs non-intervention days, the difference being indicative (p = 0.050). Pain and work exhaustion decreased during the study period, as well as HRV, although any of these changes was not statistically significant.Conclusion: For a large-scale study, it would be ideal to select assays for both major pathways: hypothalamic-pituitary-adrenal axis can be measured by cortisol, whereas response via autonomic nervous system can be measured by HRV, when roles of sympathetic and parasympathetic nervous systems can be pinpointed separately. Salivary α-amylase can be used when continuous monitoring is not possible. Psychological well-being of participants should be surveyed, as well as their activities and moods on sampling days recorded.Keywords: salivary cortisol, salivary α-amylase, heart rate variability, pain, work exhaustion

Satellite-based aerosol retrievals provide global spatially distributed estimates of atmospheric aerosol parameters that are commonly needed in applications such as estimation of atmospherically corrected satellite data products, climate modelling and air quality monitoring. However, a common feature of the conventional satellite aerosol retrievals is that they have reasonably low spatial resolution and poor accuracy caused by uncertainty in auxiliary model parameters, such as fixed aerosol model parameters, and the approximate forward radiative transfer models utilized to keep the computational complexity feasible. As a result, the improvement and reprocessing of the operational satellite data retrieval algorithms would become a tedious and computationally excessive problem. To overcome these problems, we have developed a machine-learning-based post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite retrieval data and a post-processing step where a machine learning algorithm is utilized to predict the approximation error in the conventional retrieval. With approximation error, we refer to the discrepancy between the true aerosol parameters and the ones retrieved using the satellite data. Our hypothesis is that the prediction of the approximation error with a finite training dataset is a less complex and easier task than the direct, fully learned machine-learning-based prediction in which the aerosol parameters are directly predicted given the satellite observations and measurement geometry. Our approach does not require reprocessing of the satellite retrieval products; it requires only a computationally fast machine-learning-based post-processing step of the existing retrieval product. Our approach is based on neural networks trained based on collocated satellite data and accurate ground-based Aerosol Robotic Network (AERONET) aerosol data. Based on our post-processing approach, we propose a post-process-corrected high-resolution Sentinel-3 Synergy aerosol product, which gives a spectral estimate of the aerosol optical depth at five different wavelengths with a high spatial resolution equivalent to the native resolution of the Sentinel-3 Level-1 data (300 m at nadir). With aerosol data from Sentinel-3A and 3B satellites, we demonstrate that our approach produces high-resolution aerosol data with clearly better accuracy than the operational Sentinel-3 Level-2 Synergy aerosol product, and it also results in slightly better accuracy than the conventional fully learned machine learning approach. We also demonstrate better generalization capabilities of the post-process correction approach over the fully learned approach.

In this paper, we introduce a conceptual framework for researching the dynamics of imagination in science classroom interactions. While educational interest in imagination has recently increased, prior research has not adequately accounted for how imagination is realized in and through classroom interactions, nor has it created a framework for its empirical investigation. Drawing on a theory of imagination situated in cultural psychology (Zittoun et al., 2013; Zittoun & Gillespie, 2016), we propose such a framework. We illustrate our framework with a telling case (Mitchell, 1984) of imagination from a Finnish primary science classroom community. Our illustration focuses on the dynamics of imagination as it unfolds in classroom interactions and how qualitatively distinct loops of imagination are formed. In specific, we show how the students' meaning making expands in time and space and can become more refined and differentiated through loops of imagination and their dynamics. In all, our paper argues that imagination is a constitutive element of science learning. Our proposed conceptual framework provides potential avenues for further empirical research on the dynamics of imagination in science learning and teaching.

The paper analyzes a collaborative learning process among Finnish pre-service teachers planning their own learning in a self-regulated way. The study builds on cultural-historical activity theory and the theory of expansive learning, integrating for the first time an analysis of learning actions and an analysis of types of interaction. We examine the theory of expansive learning as a possible conceptual and methodological framework for understanding this type of collaborative learning. The task of the paper is primarily methodological. We believe that cultural-historical activity theory needs to be turned into methods and procedures of systematic empirical analysis, and this article examines one such methodological solution. At the same time, we aim to uncover some substantive dynamics of expansive learning in collaborative teacher education oriented at open-ended problems and tasks. An almost complete expansive mini-cycle of learning actions appeared in the pre-service teachers' meeting. However, an analysis of the steps of formation of the shared object revealed a more complex iterative process. As the expansive learning process moved epistemically from questioning to analysis, modeling and implementation, it also moved interactionally from coordination to cooperation and communication. Yet there was no mechanical correspondence between specific learning actions and specific types of interaction. Transitions and disturbances were crucial for the dynamics of expansive learning. A full assessment of a potentially expansive mini-cycle of learning calls for extending the time scale of the analysis.

Although the number of immigrant families is increasing in Finland, the research on their perspectives on early childhood and care (ECEC) services is scarce. The objective of this small-scale case study was to increase the understanding of immigrant families' perspectives on ECEC practices. Through the qualitative content analysis of interview-based data, four themes emerged as particularly important for working with immigrant families: (a) fostering dialogue and mutual understanding; (b) promoting cultural and linguistic diversity; (c) encouraging cooperative partnership; and (d) providing support and individualized attention. This study contributes to the development of more inclusive and supportive ECEC practices in order to better support families with immigrant backgrounds.

We tested a hypothesis that in silico-discovered compounds targeting traumatic brain injury (TBI)-induced transcriptomics dysregulations will mitigate TBI-induced molecular pathology and augment the effect of co-administered antiseizure treatment, thereby alleviating functional impairment. In silico bioinformatic analysis revealed five compounds substantially affecting TBI-induced transcriptomics regulation, including calpain inhibitor, chlorpromazine, geldanamycin, tranylcypromine, and trichostatin A (TSA). In vitro exposure of neuronal-BV2-microglial co-cultures to compounds revealed that TSA had the best overall neuroprotective, antioxidative, and anti-inflammatory effects. In vivo assessment in a rat TBI model revealed that TSA as a monotherapy (1 mg/kg/d) or in combination with the antiseizure drug levetiracetam (LEV 150 mg/kg/d) mildly mitigated the increase in plasma levels of the neurofilament subunit pNF-H and cortical lesion area. The percentage of rats with seizures during 0–72 h post-injury was reduced in the following order: TBI-vehicle 80%, TBI-TSA (1 mg/kg) 86%, TBI-LEV (54 mg/kg) 50%, TBI-LEV (150 mg/kg) 40% (p < 0.05 vs. TBI-vehicle), and TBI-LEV (150 mg/kg) combined with TSA (1 mg/kg) 30% (p < 0.05). Cumulative seizure duration was reduced in the following order: TBI-vehicle 727 ± 688 s, TBI-TSA 898 ± 937 s, TBI-LEV (54 mg/kg) 358 ± 715 s, TBI-LEV (150 mg/kg) 42 ± 64 (p < 0.05 vs. TBI-vehicle), and TBI-LEV (150 mg/kg) combined with TSA (1 mg/kg) 109 ± 282 s (p < 0.05). This first preclinical intervention study on post-TBI acute seizures shows that a combination therapy with the tissue recovery enhancer TSA and LEV was safe but exhibited no clear benefit over LEV monotherapy on antiseizure efficacy. A longer follow-up is needed to confirm the possible beneficial effects of LEV monotherapy and combination therapy with TSA on chronic post-TBI structural and functional outcomes, including epileptogenesis.

Infectious diseases caused by antibiotic-resistant bacterial (ARB) pathogens are a serious threat to human and animal health. The active surveillance of ARB using an integrated one-health approach can help to reduce the emergence and spread of ARB, reduce the associated economic impact, and guide antimicrobial stewardship programs. Wastewater surveillance (WWS) of ARB provides composite samples for a total population, with easy access to the mixed community microbiome. This concept is emerging rapidly, but the clinical utility, sensitivity, and uniformity of WWS of ARB remain poorly understood especially in relation to clinical evidence in sewershed communities. Here, we systematically searched the literature to identify studies that have compared findings from WWS of ARB and antibiotic resistance genes (ARG) with clinical evidence in parallel, thereby evaluating how likely WWS of ARB and ARG can relate to the clinical cases in communities. Initially, 2,235 articles were obtained using the primary search keywords, and 1,219 articles remained after de-duplication. Among these, 35 articles fulfilled the search criteria, and an additional 13 relevant articles were included by searching references in the primary literature. Among the 48 included papers, 34 studies used a culture-based method, followed by 11 metagenomics, and three PCR-based methods. A total of 28 out of 48 included studies were conducted at the single sewershed level, eight studies involved several countries, seven studies were conducted at national or regional scales, and five at hospital levels. Our review revealed that the performance of WWS of ARB pathogens has been evaluated more frequently for Escherichia coli, Enterococcus spp., and other members of the family Enterobacteriaceae, but has not been uniformly tested for all ARB pathogens. Many wastewater-based ARB studies comparing the findings with clinical evidence were conducted to evaluate the public health risk but not to relate with clinical evidence and to evaluate the performance of WWS of ARB. Indeed, relating WWS of ARB with clinical evidence in a sewershed is not straightforward, as the source of ARB in wastewater cannot be only from symptomatic human individuals but can also be from asymptomatic carriers as well as from animal sources. Further, the varying fates of each bacterial species and ARG within the sewerage make the aim of connecting WWS of ARB with clinical evidence more complicated. Therefore, future studies evaluating the performance of many AMR pathogens and their genes for WWS one by one can make the process simpler and the interpretation of results easier.

Gene therapy would greatly benefit from a method to regulate therapeutic gene expression temporally. Riboswitches are small RNA elements that have been studied for their potential use in turning transgene expression on or off by ligand binding. We compared several tetracycline and toyocamycin-inducible ON-riboswitches for a drug responsive transgene expression. The tetracycline-dependent K19 riboswitch showed the best control and we successfully applied it to different transgenes. The induction of gene expression was 6- to 10-fold, dose-dependent, reversible, and occurred within hours after the addition of a clinically relevant tetracycline dose, using either plasmid or adeno-associated virus (AAV) vectors. To enhance the switching capacity, we further optimized the gene cassette to control the expression of a potential therapeutic gene for cardiovascular diseases, VEGF-B. Using two or three riboswitches simultaneously reduced leakiness and improved the dynamic range, and a linker sequence between the riboswitches improved their functionality. The riboswitch function was promoter-independent, but a post-transcriptional WPRE element in the expression cassette reduced its functionality. The optimized construct was a dual riboswitch at the 3′ end of the transgene with a 100 bp linker sequence. Our study reveals significant differences in the function of riboswitches and provides important aspects on optimizing expression cassette designs. The findings will benefit further research and development of riboswitches.

Today 3.7 million refugee children are out of school. The ones forcibly displaced across borders are likely to remain there much of their childhoods and go through an entire school cycle in exile. Without access to quality education, these children have diminished likelihood of breaking free from circular dependency, scarcity, and marginalization. At the same time education sectors globally are adapting to the inevitable increase of digital learning. This study was motivated by the potential availability of digital education, and it argues for non-formal digital game-based learning in refugee and low-resource environments, with a special focus on early literacy.The participants (N = 359) consisted of marginalized, most vulnerable, and out-of-school children aged between 5 and 8. They participated in interventions in Pakistan and Bangladesh for 90 days, two hours a day. The children played digital learning games at their own pace following the learning goals of their national curriculums. The learning outcomes were measured using the EGRA framework.The study found that the intervention children achieved or surpassed the learning gains of a control group studying through formal education. These results suggest that digital learning games show promise for improving early grade literacy, even in low-resource contexts.

Abstract Background The genetics of cerebellar ataxia is complex. Hundreds of causative genes have been identified, but only a few cause more than single cases. The spectrum of ataxia-causing genes differs considerably between populations. The aim of the study was to investigate the molecular epidemiology of ataxia in the Finnish population. Patients and methods All patients in hospital database were reviewed for the diagnosis of unspecified ataxia. Acquired ataxias and nongenetic ataxias such as those related to infection, trauma or stroke were excluded. Sixty patients with sporadic ataxia with unknown etiology and 36 patients with familial ataxia of unknown etiology were recruited in the study. Repeat expansions in the SCA genes (ATXN1, 2, 3, 7, 8/OS, CACNA1A, TBP), FXN, and RFC1 were determined. Point mutations in POLG, SPG7 and in mitochondrial DNA (mtDNA) were investigated. In addition, DNA from 8 patients was exome sequenced. Results A genetic cause of ataxia was found in 33 patients (34.4%). Seven patients had a dominantly inherited repeat expansion in ATXN8/OS. Ten patients had mitochondrial ataxia resulting from mutations in nuclear mitochondrial genes POLG or RARS2, or from a point mutation m.8561C > G or a single deletion in mtDNA. Interestingly, five patients were biallelic for the recently identified pathogenic repeat expansion in RFC1. All the five patients presented with the phenotype of cerebellar ataxia, neuropathy, and vestibular areflexia (CANVAS). Moreover, screening of 54 patients with Charcot-Marie-Tooth neuropathy revealed four additional patients with biallelic repeat expansion in RFC1, but none of them had cerebellar symptoms. Conclusions Expansion in ATXN8/OS results in the majority of dominant ataxias in Finland, while mutations in RFC1 and POLG are the most common cause of recessive ataxias. Our results suggest that analysis of RFC1 should be included in the routine diagnostics of idiopathic ataxia and Charcot-Marie-Tooth polyneuropathy.

A quantitative comparison study for Raman lidar and ceilometer observations, and for model simulations of mass concentration estimates of smoke particles is presented. Layers of biomass burning aerosol particles were observed in the lower troposphere, at 2 to 5 km height on 4 to 6 June 2019, over Kuopio, Finland. These long-range-transported smoke particles originated from a Canadian wildfire event. The most pronounced smoke plume detected on 5 June was intensively investigated. Optical properties were retrieved from the multi-wavelength Raman polarization lidar PollyXT. Particle linear depolarization ratios (PDRs) of this plume were measured to be 0.08±0.02 at 355 nm and 0.05±0.01 at 532 nm, suggesting the presence of partly coated soot particles or particles that have mixed with a small amount of dust or other non-spherical aerosol type. The layer-mean PDR at 355 nm (532 nm) decreased during the day from ∼ 0.11 (0.06) in the morning to ∼ 0.05 (0.04) in the evening; this decrease with time could be linked to the particle aging and related changes in the smoke particle shape properties. Lidar ratios were derived as 47±5 sr at 355 nm and 71±5 sr at 532 nm. A complete ceilometer data processing for a Vaisala CL51 ceilometer is presented from a sensor-provided attenuated backscatter coefficient to particle mass concentration (including the water vapor correction for high latitude for the first time). Aerosol backscatter coefficients (BSCs) were measured at four wavelengths (355, 532, 1064 nm from PollyXT and 910 nm from CL51). Two methods, based on a combined lidar and sun-photometer approach, are applied for mass concentration estimations from both PollyXT and the ceilometer CL51 observations. In the first method, no. 1, we used converted BSCs at 532 nm (from measured BSCs) by corresponding measured backscatter-related Ångström exponents, whereas in the second method, no. 2, we used measured BSCs at each wavelength independently. A difference of ∼ 12 % or ∼ 36 % was found between PollyXT and CL51 estimated mass concentrations using method no. 1 or no. 2, showing the potential of mass concentration estimates from a ceilometer. Ceilometer estimations have an uncertainty of ∼ 50 % in the mass retrieval, but the potential of the data lies in the great spatial coverage of these instruments. The mass retrievals were compared with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorological and aerosol reanalysis. The inclusion of dust (as indicated by MERRA-2 data) in the retrieved mass concentration is negligible considering the uncertainties, which also shows that ceilometer observations for mass retrievals can be used even without exact knowledge of the composition of the smoke-dominated aerosol plume in the troposphere.

Vegetation emits organic vapors which can form aerosols in the atmosphere and influence cloud properties. Here, the authors show observational evidence that warmer temperatures lead to increased emissions of these aerosols in boreal forests which cause surface cooling, demonstrating a negative climate feedback mechanism.

Satellite estimates of surface UV irradiance have been available since 1978 from the TOMS UV spectrometer and have continued with significantly improved ground resolution using the Ozone Monitoring Instrument (OMI 2004–current) and Sentinel 5 Precursor (S5P 2017–current). The surface UV retrieval algorithm remains essentially the same: it first estimates the clear-sky UV irradiance based on measured ozone and then accounts for the attenuation by clouds and aerosols, applying two consecutive correction factors. When estimating the total aerosol effect in surface UV irradiance, there are two major classes of aerosols to be considered: (1) aerosols that only scatter UV radiation and (2) aerosols that both scatter and absorb UV radiation. The former effect is implicitly included in the measured effective Lambertian-equivalent scene reflectivity (LER), so the scattering aerosol influence is estimated through cloud correction factor. Aerosols that absorb UV radiation attenuate the surface UV radiation more strongly than non-absorbing aerosols of the same extinction optical depth. Moreover, since these aerosols also attenuate the outgoing satellite-measured radiance, the cloud correction factor that treats these aerosols as purely scattering underestimates their aerosol optical depth (AOD), causing underestimation of LER and overestimation of surface UV irradiance. Therefore, for correction of aerosol absorption, additional information is needed, such as a model-based monthly climatology of aerosol absorption optical depth (AAOD). A correction for absorbing aerosols was proposed almost a decade ago and later implemented in the operational OMI and TROPOMI UV algorithms. In this study, however, we show that there is still room for improvement to better account for the solar zenith angle (SZA) dependence and nonlinearity in the absorbing aerosol attenuation, and as a result we propose an improved correction scheme. There are two main differences between the new proposed correction and the one that is currently operational in OMI and TROPOMI UV algorithms. First, the currently operational correction for absorbing aerosols is a function of AAOD only, while the new correction additionally takes the solar zenith angle dependence into account. Second, the second-order polynomial of the new correction takes the nonlinearity in the correction as a function of AAOD better into account, if compared to the currently operational one, and thus better describes the effect by absorbing aerosols over a larger range of AAOD. To illustrate the potential impact of the new correction in the global UV estimates, we applied the current and new proposed correction for global fields of AAOD from the aerosol climatology currently used in OMI UV algorithm, showing a typical differences of ±5 %. This new correction is easy to implement operationally using information of solar zenith angle and existing AAOD climatology.

BackgroundThe detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated. ObjectiveThe purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being. MethodsPatients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics. ResultsOf the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively. ConclusionsThe 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. Trial RegistrationClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335

Satellite-based aerosol retrievals provide a timely view of atmospheric aerosol properties, having a crucial role in the subsequent estimation of air quality indicators, atmospherically corrected satellite data products, and climate applications. However, current aerosol data products based on satellite data often have relatively large biases compared to accurate ground-based measurements and distinct uncertainty levels associated with them. These biases and uncertainties are often caused by oversimplified assumptions and approximations used in the retrieval algorithms due to unknown surface reflectance or fixed aerosol models. Moreover, the retrieval algorithms do not usually take advantage of all the possible observational data collected by the satellite instruments and may, for example, leave some spectral bands unused. The improvement and the re-processing of the past and current operational satellite data retrieval algorithms would become tedious and computationally expensive. To overcome this burden, we have developed a model-enforced post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite aerosol retrievals and a post-processing step carried out with a machine-learning-based correction model for the approximation error in the retrieval. The developed approach allows for the utilization of auxiliary data sources, such as meteorological information, or additional observations such as spectral bands unused by the original retrieval algorithm. The post-process correction model can learn to correct for the biases and uncertainties in the original retrieval algorithms. As the correction is carried out as a post-processing step, it allows for computationally efficient re-processing of existing satellite aerosol datasets without fully re-processing the much larger original radiance data. We demonstrate with over-land aerosol optical depth (AOD) and Ångström exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of the Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. For instance, in our evaluation, the number of AOD samples within the MODIS Dark Target expected error envelope increased from 63 % to 85 % when the post-process correction was applied. In addition to method description and accuracy results, we also give recommendations for validating machine-learning-based satellite data products.

The emergence of new variants of SARS-CoV-2 associated with varying infectivity, pathogenicity, diagnosis, and effectiveness against treatments challenged the overall management of the COVID-19 pandemic. Wastewater surveillance (WWS), i.e., monitoring COVID-19 infections in communities through detecting viruses in wastewater, was applied to track the emergence and spread of SARS-CoV-2 variants globally. However, there is a lack of comprehensive understanding of the use and effectiveness of WWS for new SARS-CoV-2 variants. Here we systematically reviewed published articles reporting monitoring of different SARS-CoV-2 variants in wastewater by following the PRISMA guidelines and provided the current state of the art of this study area. A total of 80 WWS studies were found that reported different monitoring variants of SARS-CoV-2 until November 2022. Most of these studies (66 out of the total 80, 82.5%) were conducted in Europe and North America, i.e., resource-rich countries. There was a high variation in WWS sampling strategy around the world, with composite sampling (50/66 total studies, 76%) as the primary method in resource-rich countries. In contrast, grab sampling was more common (8/14 total studies, 57%) in resource-limited countries. Among detection methods, the reverse transcriptase polymerase chain reaction (RT-PCR)-based sequencing method and quantitative RT-PCR method were commonly used for monitoring SARS-CoV-2 variants in wastewater. Among different variants, the B1.1.7 (Alpha) variant that appeared earlier in the pandemic was the most reported (48/80 total studies), followed by B.1.617.2 (Delta), B.1.351 (Beta), P.1 (Gamma), and others in wastewater. All variants reported in WWS studies followed the same pattern as the clinical reporting within the same timeline, demonstrating that WWS tracked all variants in a timely way when the variants emerged. Thus, wastewater monitoring may be utilized to identify the presence or absence of SARS-CoV-2 and follow the development and transmission of existing and emerging variants. Routine wastewater monitoring is a powerful infectious disease surveillance tool when implemented globally.

Analysis platforms to predict drug-induced seizure liability at an early phase of drug development would improve safety and reduce attrition and the high cost of drug development. We hypothesized that a drug-induced in vitro transcriptomics signature predicts its ictogenicity. We exposed rat cortical neuronal cultures to non-toxic concentrations of 34 compounds for 24 h; 11 were known to be ictogenic (tool compounds), 13 were associated with a high number of seizure-related adverse event reports in the clinical FDA Adverse Event Reporting System (FAERS) database and systematic literature search (FAERS-positive compounds), and 10 were known to be non-ictogenic (FAERS-negative compounds). The drug-induced gene expression profile was assessed from RNA-sequencing data. Transcriptomics profiles induced by the tool, FAERS-positive and FAERS-negative compounds, were compared using bioinformatics and machine learning. Of the 13 FAERS-positive compounds, 11 induced significant differential gene expression; 10 of the 11 showed an overall high similarity to the profile of at least one tool compound, correctly predicting the ictogenicity. Alikeness-% based on the number of the same differentially expressed genes correctly categorized 85%, the Gene Set Enrichment Analysis score correctly categorized 73%, and the machine-learning approach correctly categorized 91% of the FAERS-positive compounds with reported seizure liability currently in clinical use. Our data suggest that the drug-induced gene expression profile could be used as a predictive biomarker for seizure liability.