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The pathophysiology of major depressive disorder (MDD) encompasses an array of changes at molecular and neurobiological levels. As chronic stress promotes neurotoxicity there are alterations in the expression of genes and gene-regulatory molecules. The hippocampus is particularly sensitive to the effects of stress and its posterior volumes can deliver clinically valuable information about the outcomes of antidepressant treatment. In the present work, we analyzed individuals with MDD (N = 201) and healthy controls (HC = 104), as part of the CAN-BIND-1 study. We used magnetic resonance imaging (MRI) to measure hippocampal volumes, evaluated gene expression with RNA sequencing, and assessed DNA methylation with the (Infinium MethylationEpic Beadchip), in order to investigate the association between hippocampal volume and both RNA expression and DNA methylation. We identified 60 RNAs which were differentially expressed between groups. Of these, 21 displayed differential methylation, and seven displayed a correlation between methylation and expression. We found a negative association between expression of Brain Abundant Membrane Attached Signal Protein 1 antisense 1 RNA (BASP1-AS1) and right hippocampal tail volume in the MDD group (β = −0.218, p = 0.021). There was a moderating effect of the duration of the current episode on the association between the expression of BASP1-AS1 and right hippocampal tail volume in the MDD group (β = −0.48, 95% C.I. [−0.80, −0.16]. t = −2.95 p = 0.004). In conclusion, we found that overexpression of BASP1-AS1 was correlated with DNA methylation, and was negatively associated with right tail hippocampal volume in MDD.

Microplastics (MPs) have been increasingly identified as a global concern because of their ubiquitous distribution and the threats they pose. MPs embedded in the solid matrix remain an underexplored field compared to MPs in aquatic environments. This review focuses on solid-embedded MPs in a binary system consisting of sewage sludge and agricultural soils, which are interconnected through land application of sewage sludge to agricultural soils. The detection, occurrence, and impacts of MPs were summarized for sewage sludge and agricultural soils. Specifically, the latest techniques for detecting solid-embedded MPs are summarized and compared. The occurrence of MPs is presented from three aspects, i.e., the origin, transformation, and properties. Lastly, the impacts of solid-embedded MPs on sewage sludge treatment processes and the agroecosystem are reviewed. As a cutting-edge overview of the current knowledge, this review proposes key challenges to be further investigated with respect to solid-embedded MPs, including a uniform detection procedure, worldwide monitoring programs, rigorous assessments of potential risks, and mitigation measures through collaborative efforts by related liable parties.

Wastewater surveillance of pathogens may be a useful tool to help determine whether clinical surveillance of disease is effective or inadequate due to under-reporting and under-detection. In addition, tracking of pathogen concentrations over time could potentially provide a measure of the effectiveness of public health control measures and the impact of the gradual relaxation of these controls. Analysis of wastewater using quantitative molecular methods offers a real-time measure of infections in the community, and thus is expected to provide a more sensitive and rapid indication of changes in infection rates before such effects become detectable by clinical health surveillance. Models may help to back-calculate wastewater prevalence to population prevalence or to correct pathogen counts for wastewater catchment-specific and temporal effects. They may also help to design the wastewater sampling strategy. This article provides a brief summary of the history of pathogen wastewater surveillance to help set the context for the SARS-CoV-2 wastewater-based epidemiology (WBE) programmes currently being undertaken globally. HIGHLIGHTS A brief history of pathogen wastewater surveillance.; Context for current SARS-CoV-2 wastewater-based epidemiology (WBE) programmes.; Recommendations on research and investigation priorities.; Wide potential of modelling is presented together with remaining knowledge gaps.

Despite the wide application of the magnetic resonance imaging (MRI) technique, there are no widely used standards on naming and describing MRI sequences. The absence of consistent naming conventions presents a major challenge in automating image processing since most MRI software require a priori knowledge of the type of the MRI sequences to be processed. This issue becomes increasingly critical with the current efforts toward open-sharing of MRI data in the neuroscience community. This manuscript reports an MRI sequence detection method using imaging metadata and a supervised machine learning technique. Three datasets from the Brain Center for Ontario Data Exploration (Brain-CODE) data platform, each involving MRI data from multiple research institutes, are used to build and test our model. The preliminary results show that a random forest model can be trained to accurately identify MRI sequence types, and to recognize MRI scans that do not belong to any of the known sequence types. Therefore the proposed approach can be used to automate processing of MRI data that involves a large number of variations in sequence names, and to help standardize sequence naming in ongoing data collections. This study highlights the potential of the machine learning approaches in helping manage health data.

Highlights • MTBI is poorly understood and lacks objective diagnostic and prognostic tools. • Abnormal neural oscillations are found in subjects with a history of mTBI. • We identify transient bursts in MEG data using a Hidden Markov Model. • We explain a deficit in beta connectivity and power in terms of transient bursts. • Data-driven feature selection identifies symptom-relevant functional connections., Mild traumatic brain injury (mTBI) poses a considerable burden on healthcare systems. Whilst most patients recover quickly, a significant number suffer from sequelae that are not accompanied by measurable structural damage. Understanding the neural underpinnings of these debilitating effects and developing a means to detect injury, would address an important unmet clinical need. It could inform interventions and help predict prognosis. Magnetoencephalography (MEG) affords excellent sensitivity in probing neural function and presents significant promise for assessing mTBI, with abnormal neural oscillations being a potential specific biomarker. However, growing evidence suggests that neural dynamics are (at least in part) driven by transient, pan-spectral bursting and in this paper, we employ this model to investigate mTBI. We applied a Hidden Markov Model to MEG data recorded during resting state and a motor task and show that previous findings of diminished intrinsic beta amplitude in individuals with mTBI are largely due to the reduced beta band spectral content of bursts, and that diminished beta connectivity results from a loss in the temporal coincidence of burst states. In a motor task, mTBI results in diminished burst amplitude, altered modulation of burst probability during movement, and a loss in connectivity in motor networks. These results suggest that, mechanistically, mTBI disrupts the structural framework underlying neural synchrony, which impairs network function. Whilst the damage may be too subtle for structural imaging to see, the functional consequences are detectable and persist after injury. Our work shows that mTBI impairs the dynamic coordination of neural network activity and proposes a potent new method for understanding mTBI.

The shedding of pathogens by infected humans enables the use of sewage monitoring to conduct wastewater-based epidemiology (WBE). Although most WBE studies use data from large sewage treatment plants, timely data from smaller catchments are needed for targeted public health action. Traditional sampling methods, like autosamplers or grab sampling, are not conducive to quick ad hoc deployments and high-resolution monitoring at these smaller scales. This study develops and validates a cheap and easily deployable passive sampler unit, made from readily available consumables, with relevance to the COVID-19 pandemic but with broader use for WBE. We provide the first evidence that passive samplers can be used to detect SARS-CoV-2 in wastewater from populations with low prevalence of active COVID-19 infections (0.034 to 0.34 per 10,000), demonstrating their ability for early detection of infections at three different scales (lot, suburb, and city). A side by side evaluation of passive samplers (n = 245) and traditionally collected wastewater samples (n = 183) verified that the passive samplers were sensitive at detecting SARS-CoV-2 in wastewater. On all 33 days where we directly compared traditional and passive sampling techniques, at least one passive sampler was positive when the average SARS-CoV-2 concentration in the wastewater equaled or exceeded the quantification limit of 1.8 gene copies per mL (n = 7). Moreover, on 13 occasions where wastewater SARS-CoV-2 concentrations were less than 1.8 gene copies per mL, one or more passive samplers were positive. Finally, there was a statistically significant (p < 0.001) positive relationship between the concentrations of SARS-CoV-2 in wastewater and the levels found on the passive samplers, indicating that with further evaluation, these devices could yield semi-quantitative results in the future. Passive samplers have the potential for wide use in WBE with attractive feasibility attributes of cost, ease of deployment at small-scale locations, and continuous sampling of the wastewater. Further research will focus on the optimization of laboratory methods including elution and extraction and continued parallel deployment and evaluations in a variety of settings to inform optimal use in wastewater surveillance., Wastewater surveillance can track the spread of infectious diseases, and our paper presents a sensitive, simple, and cost-effective method called passive sampling.

Finding a clinically useful neuroimaging biomarker that can predict treatment response in patients with major depressive disorder (MDD) is challenging, in part because of poor reproducibility and generalizability of findings across studies. Previous work has suggested that posterior hippocampal volumes in depressed patients may be associated with antidepressant treatment outcomes. The primary purpose of this investigation was to examine further whether posterior hippocampal volumes predict remission following antidepressant treatment. Magnetic resonance imaging (MRI) scans from 196 patients with MDD and 110 healthy participants were obtained as part of the first study in the Canadian Biomarker Integration Network in Depression program (CAN-BIND 1) in which patients were treated for 16 weeks with open-label medication. Hippocampal volumes were measured using both a manual segmentation protocol and FreeSurfer 6.0. Baseline hippocampal tail (Ht) volumes were significantly smaller in patients with depression compared to healthy participants. Larger baseline Ht volumes were positively associated with remission status at weeks 8 and 16. Participants who achieved early sustained remission had significantly greater Ht volumes compared to those who did not achieve remission by week 16. Ht volume is a prognostic biomarker for antidepressant treatment outcomes in patients with MDD.

Background While response to antidepressants in major depressive disorder is difficult to predict, characterizing the organization and integrity of white matter in the brain with diffusion tensor imaging (DTI) may provide the means to distinguish between antidepressant responders and nonresponders. Methods DTI data were collected at 6 sites (Canadian Biomarker Integration Network in Depression-1 [CAN-BIND-1 study]) from 200 (127 women) depressed and 112 (71 women) healthy participants at 3 time points: at baseline, 2 weeks, and 8 weeks following initiation of selective serotonin reuptake inhibitor treatment. Therapeutic response was established by a 50% reduction of symptoms at 8 weeks. Analysis on responders, nonresponders, and control subjects yielded 4 scalar metrics: fractional anisotropy and mean, axial, and radial diffusivity. Region-of-interest analysis was carried out on 40 white matter regions using a skeletonization approach. Mixed-effects regression was incorporated to test temporal trends. Results The data acquired at baseline showed that axial diffusivity in the external capsule, which overlaps the superior longitudinal fasciculus, was significantly associated with medication response. Regression analysis revealed further baseline differences of responders compared with nonresponders in the cingulum regions, sagittal stratum, and corona radiata. Additional group differences relative to control subjects were seen in the internal capsule, posterior thalamic radiation, and uncinate fasciculus. Most effect sizes were moderate (near 0.5), with a maximum of 0.76 in the cingulum-hippocampus region. No temporal changes in DTI metrics were observed over the 8-week study period. Conclusions Several DTI measures of altered white matter specifically distinguished medication responders and nonresponders at baseline and show promise for predicting treatment response in depression.

Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convolutional neural networks (CNN) have emerged as a promising solution for large longitudinal neuroimaging studies. However, for these novel algorithms to be useful in clinical studies, the accuracy and reproducibility should be established on independent datasets. Here, we evaluate the performance of a CNN-based hippocampal segmentation algorithm that was developed by Thyreau and colleagues – Hippodeep. We compared its segmentation outputs to manual segmentation and FreeSurfer 6.0 in a sample of 200 healthy participants scanned repeatedly at seven sites across Canada, as part of the Canadian Biomarker Integration Network in Depression consortium. The algorithm demonstrated high levels of stability and reproducibility of volumetric measures across all time points compared to the other two techniques. Although more rigorous testing in clinical populations is necessary, this approach holds promise as a viable option for tracking volumetric changes in longitudinal neuroimaging studies.

Studies of clinical populations that combine MRI data generated at multiple sites are increasingly common. The Canadian Biomarker Integration Network in Depression (CAN-BIND; www.canbind.ca) is a national depression research program that includes multimodal neuroimaging collected at several sites across Canada. The purpose of the current paper is to provide detailed information on the imaging protocols used in a number of CAN-BIND studies. The CAN-BIND program implemented a series of platform-specific MRI protocols, including a suite of prescribed structural and functional MRI sequences supported by real-time monitoring for adherence and quality control. The imaging data are retained in an established informatics and databasing platform. Approximately 1300 participants are being recruited, including almost 1000 with depression. These include participants treated with antidepressant medications, transcranial magnetic stimulation, cognitive behavioural therapy and cognitive remediation therapy. Our ability to analyze the large number of imaging variables available may be limited by the sample size of the substudies. The CAN-BIND program includes a multimodal imaging database supported by extensive clinical, demographic, neuropsychological and biological data from people with major depression. It is a resource for Canadian investigators who are interested in understanding whether aspects of neuroimaging — alone or in combination with other variables — can predict the outcomes of various treatment modalities.

A wide range of cyanobacterial species and their harmful metabolites are increasingly detected in water bodies worldwide, exacerbated by climate change and human activities. The resulting bloom conditions represent significant challenges to production of safe drinking water and cost effective water reuse, therefore their removal is a priority to ensure public safety. While current microscopic taxonomy identification methods provide valuable information about cell numbers during treatment, these methods are incapable of providing information about the fate of cells during treatment. The objectives of this study were to (1) identify the critical control points for breakthrough and accumulation of cells by investigating the fate of cells during treatment processes using a combination of taxonomy, cell integrity and next-generation sequencing (NGS), and (2) assess the impact of pre-treatment processes on breakthrough prevention at critical control points, and the benefits of cell integrity and NGS analysis for improved management purposes. This paper presents the results of an unprecedented cyanobacterial monitoring program conducted in four full scale water treatment plants located in three different climate zones. Cyanobacterial cell integrity and accumulation during operation process were assessed for the first time using next generation of gene sequencing methods. NGS analysis led to detection of cyanobacterial and melainabacteria orders in water samples that were not identified by microscopy. 80 ± 5% of cells were completely lysed post pre-oxidation (for both ozone and potassium permanganate). However unlike pre-ozonation, the remaining cells were undamaged cells with the potential to accumulate and grow within the plants post-KMnO4 treatment, particularly in clarifier sludge. To effectively monitor water quality, this study presents a synergistic approach coupling new and traditional analytical methods and demonstrates the importance of identifying critical points for managing accumulation of cyanobacteria within plants.

Understanding the neural underpinnings of major depressive disorder (MDD) and its treatment could improve treatment outcomes. So far, findings are variable and large sample replications scarce. We aimed to replicate and extend altered functional connectivity associated with MDD and pharmacotherapy outcomes in a large, multisite sample. Resting-state fMRI data were collected from 129 patients and 99 controls through the Canadian Biomarker Integration Network in Depression. Symptoms were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). Connectivity was measured as correlations between four seeds (anterior and posterior cingulate cortex, insula and dorsolateral prefrontal cortex) and all other brain voxels. Partial least squares was used to compare connectivity prior to treatment between patients and controls, and between patients reaching remission (MADRS ≤ 10) early (within 8 weeks), late (within 16 weeks), or not at all. We replicated previous findings of altered connectivity in patients. In addition, baseline connectivity of the anterior/posterior cingulate and insula seeds differentiated patients with different treatment outcomes. The stability of these differences was established in the largest single-site subsample. Our replication and extension of altered connectivity highlighted previously reported and new differences between patients and controls, and revealed features that might predict remission prior to pharmacotherapy. Trial registration:ClinicalTrials.gov: NCT01655706.

Objectives Recent studies suggest that interindividual genetic differences in glial-dependent CSF flow through the brain parenchyma, known as glymphatic flow, may trigger compensatory changes in human sleep physiology. In animal models, brain perivascular spaces are a critical conduit for glymphatic flow. We tested the hypothesis that MRI-visible PVS volumes, a putative marker of perivascular dysfunction, are associated with compensatory differences in real-world human sleep behavior. Methods We analyzed data from 152 cerebrovascular disease patients from the Ontario Neurodegenerative Disease Research Initiative (ONDRI). PVS volumes were measured using 3T-MRI. Self-reported total sleep time, time in bed, and daytime dysfunction were extracted from the Pittsburgh Sleep Quality Index. Results Individuals with greater PVS volumes reported longer time in bed (+0.85 h per log10 proportion of intracranial volume (ICV) occupied by PVS, SE = 0.30, p = 0.006) and longer total sleep times (+0.70 h per log10 proportion of ICV occupied by PVS volume, SE = 0.33, p = 0.04), independent of vascular risk factors, sleep apnea, nocturnal sleep disturbance, depression, and global cognitive status. Further analyses suggested that the positive association between PVS volumes and total sleep time was mediated by greater time in bed. Moreover, despite having on average greater total sleep times, individuals with greater basal ganglia PVS volumes were more likely to report daytime dysfunction (OR 5.63 per log10 proportion of ICV occupied by PVS, 95% CI: 1.38–22.26, p = 0.018). Conclusions Individuals with greater PVS volumes spend more time in bed, resulting in greater total sleep time, which may represent a behavioral compensatory response to perivascular space dysfunction.

With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3–85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3–20 years) for afni_refacer and the oldest (44–85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files.

Conventional processes (coagulation, flocculation, sedimentation, and filtration) are widely used in drinking water treatment plants and are considered a good treatment strategy to eliminate cyanobacterial cells and cell-bound cyanotoxins. The diversity of cyanobacteria was investigated using taxonomic cell counts and shotgun metagenomics over two seasons in a drinking water treatment plant before, during, and after the bloom. Changes in the community structure over time at the phylum, genus, and species levels were monitored in samples retrieved from raw water (RW), sludge in the holding tank (ST), and sludge supernatant (SST). Aphanothece clathrata brevis, Microcystis aeruginosa, Dolichospermum spiroides, and Chroococcus minimus were predominant species detected in RW by taxonomic cell counts. Shotgun metagenomics revealed that Proteobacteria was the predominant phylum in RW before and after the cyanobacterial bloom. Taxonomic cell counts and shotgun metagenomic showed that the Dolichospermum bloom occurred inside the plant. Cyanobacteria and Bacteroidetes were the major bacterial phyla during the bloom. Shotgun metagenomics also showed that Synechococcus, Microcystis, and Dolichospermum were the predominant detected cyanobacterial genera in the samples. Conventional treatment removed more than 92% of cyanobacterial cells but led to cell accumulation in the sludge up to 31 times more than in the RW influx. Coagulation/sedimentation selectively removed more than 96% of Microcystis and Dolichospermum. Cyanobacterial community in the sludge varied from raw water to sludge during sludge storage (1&ndash, 13 days). This variation was due to the selective removal of coagulation/sedimentation as well as the accumulation of captured cells over the period of storage time. However, the prediction of the cyanobacterial community composition in the SST remained a challenge. Among nutrient parameters, orthophosphate availability was related to community profile in RW samples, whereas communities in ST were influenced by total nitrogen, Kjeldahl nitrogen (N- Kjeldahl), total and particulate phosphorous, and total organic carbon (TOC). No trend was observed on the impact of nutrients on SST communities. This study profiled new health-related, environmental, and technical challenges for the production of drinking water due to the complex fate of cyanobacteria in cyanobacteria-laden sludge and supernatant.

Quality control of morphometric neuroimaging data is essential to improve reproducibility. Owing to the complexity of neuroimaging data and subsequently the interpretation of their results, visual inspection by trained raters is the most reliable way to perform quality control. Here, we present a protocol for visual quality control of the anatomical accuracy of FreeSurfer parcellations, based on an easy-to-use open source tool called VisualQC. We comprehensively evaluate its utility in terms of error detection rate and inter-rater reliability on two large multi-site datasets, and discuss site differences in error patterns. This evaluation shows that VisualQC is a practically viable protocol for community adoption.

Anhedonia is thought to reflect deficits in reward processing that are associated with abnormal activity in mesocorticolimbic brain regions. It is expressed clinically as a deficit in the interest or pleasure in daily activities. More severe anhedonia in major depressive disorder (MDD) is a negative predictor of antidepressant response. It is unknown, however, whether the pathophysiology of anhedonia represents a viable avenue for identifying biological markers of antidepressant treatment response. Therefore, this study aimed to examine the relationships between reward processing and response to antidepressant treatment using clinical, behavioral, and functional neuroimaging measures. Eighty-seven participants in the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) protocol received 8 weeks of open-label escitalopram. Clinical correlates of reward processing were assessed at baseline using validated scales to measure anhedonia, and a monetary incentive delay (MID) task during functional neuroimaging was completed at baseline and after 2 weeks of treatment. Response to escitalopram was associated with significantly lower self-reported deficits in reward processing at baseline. Activity during the reward anticipation, but not the reward consumption, phase of the MID task was correlated with clinical response to escitalopram at week 8. Early (baseline to week 2) increases in frontostriatal connectivity during reward anticipation significantly correlated with reduction in depressive symptoms after 8 weeks of treatment. Escitalopram response is associated with clinical and neuroimaging correlates of reward processing. These results represent an important contribution towards identifying and integrating biological, behavioral, and clinical correlates of treatment response. ClinicalTrials.gov: NCT01655706.

Large scale research studies combining magnetic resonance imaging data generated at multiple sites on multiple vendor platforms are becoming more commonplace. The Ontario Neurodegenerative Disease Research Initiative (ONDRI - http://ondri.ca/), a project funded by the Ontario Brain Institute (OBI), is a recently established province-wide natural history study, which has recruited more than 500 participants from neurodegenerative disease groups including amyotrophic lateral sclerosis, fronto-temporal dementia, Parkinson’s disease, Alzheimer’s disease, mild cognitive impairment, and cerebrovascular disease (previously referred to as the vascular cognitive impairment cohort). Because of its multi-site nature, all captured data must be standardized and meet minimum quality standards to reduce variability. The goal of the ONDRI imaging platform is to maximize data quality by implementing vendor-specific harmonized MR imaging protocols (consistent with the Canadi-an Dementia Imaging Protocol - http://www.cdip-pcid.ca/), monitoring protocol adherence, qualitatively assessing image quality, measuring signal-to-noise and contrast-to-noise, monitoring system stability, and applying corrections based on the analysis of images from two different phantoms regularly acquired at each site. To maximize image quality, this work describes the use of various automatic pipelines and manual assessment steps, integrated within an established informatics and databasing platform, the Stroke Patient Recovery Research Database (SPReD) built on the Extensible Neuroimaging Archive Toolkit (XNAT), and contained within the Brain-CODE (Centre for Ontario Data Exploration) framework. The purpose of the current paper is to describe the steps undertaken by ONDRI to achieve this high standard of data integrity. Data have been successfully collected for the past 4 years with the pipelines and assessments identifying deviations, allowing for timely interventions and assessment of image quality.

Highlights • Patients with major depression underwent two-stage 16-week antidepressant trial. • Whole-brain cortical thickness (CT) was measured in patients and healthy controls. • Site (N = 6) differences were corrected at each vertex using ComBat. • Patients exhibited thinner cortex in left rostral middle frontal cortex at baseline. • There were no baseline CT features associated with antidepressant response., Major depressive disorder (MDD) is considered a highly heterogeneous clinical and neurobiological mental disorder. We employed a novel layered treatment design to investigate whether cortical thickness features at baseline differentiated treatment responders from non-responders after 8 and 16 weeks of a standardized sequential antidepressant treatment. Secondary analyses examined baseline differences between MDD and controls as a replication analysis and longitudinal changes in thickness after 8 weeks of escitalopram treatment. 181 MDD and 95 healthy comparison (HC) participants were studied. After 8 weeks of escitalopram treatment (10–20 mg/d, flexible dosage), responders (>50% decrease in Montgomery-Åsberg Depression Scale score) were continued on escitalopram; non-responders received adjunctive aripiprazole (2–10 mg/d, flexible dosage). MDD participants were classified into subgroups according to their response profiles at weeks 8 and 16. Baseline group differences in cortical thickness were analyzed with FreeSurfer between HC and MDD groups as well as between response groups. Two-stage longitudinal processing was used to investigate 8-week escitalopram treatment-related changes in cortical thickness. Compared to HC, the MDD group exhibited thinner cortex in the left rostral middle frontal cortex [MNI(X,Y,Z=−29,9,54.5,−7.7); CWP=0.0002]. No baseline differences in cortical thickness were observed between responders and non-responders based on week-8 or week-16 response profile. No changes in cortical thickness was observed after 8 weeks of escitalopram monotherapy. In a two-step 16-week sequential clinical trial we found that baseline cortical thickness does not appear to be associated to clinical response to pharmacotherapy at 8 or 16 weeks.

Cyanobacterial blooms produce nuisance metabolites (e.g., cyanotoxins and T&O compounds) thereby posing water quality management issues for aquatic sources used for potable water production, aquaculture, and recreation. A variety of in-lake/reservoir control measures are implemented to reduce the abundance of nuisance cyanobacteria biomass or decrease the amount of available phosphorous (P). This paper critically reviews the chemical control strategies implemented for in-lake/reservoir management of cyanobacterial blooms, i.e., algaecides and nutrient sequestering coagulants/flocculants, by highlighting (i) their mode of action, (ii) cases of successful and unsuccessful treatment, (iii) and factors influencing performance (e.g., water quality, process control techniques, source water characteristics, etc.). Algaecides generally result in immediate improvements in water quality and offer selective cyanobacterial control when peroxide-based alagecides are used. However, they have a range of limitations: causing cell lysis and release of cyanotoxins, posing negative impacts on aquatic plants and animals, leaving behind environmentally relevant treatment residuals (e.g., Cu in water and sediments), and offering only short-term bloom control characterized by cyanobacterial rebound. Coagulants/flocculants (alum, iron, calcium, and lanthanum bentonite) offer long-term internal nutrient control when external nutrient loading is controlled. Treatment performance is often influenced by background water quality conditions, and source water characteristics (e.g., surface area, depth, mixing regimes, and residence time). The reviewed case studies highlight that external nutrient load reduction is the most fundamental aspect of cyanobacterial control. None of the reviewed control strategies provide a comprehensive solution to cyanobacterial blooms.

Many drinking water utilities drawing from waters susceptible to harmful algal blooms (HABs) are implementing monitoring tools that can alert them to the onset of blooms. Some have invested in fluorescence-based online monitoring probes to measure phycocyanin, a pigment found in cyanobacteria, but it is not clear how to best use the data generated. Previous studies have focused on correlating phycocyanin fluorescence and cyanobacteria cell counts. However, not all utilities collect cell count data, making this method impossible to apply in some cases. Instead, this paper proposes a novel approach to determine when a utility needs to respond to a HAB based on machine learning by identifying anomalies in phycocyanin fluorescence data without the need for corresponding cell counts or biovolume. Four widespread and open source algorithms are evaluated on data collected at four buoys in Lake Erie from 2014 to 2019: local outlier factor (LOF), One-Class Support Vector Machine (SVM), elliptic envelope, and Isolation Forest (iForest). When trained on standardized historical data from 2014 to 2018 and tested on labelled 2019 data collected at each buoy, the One-Class SVM and elliptic envelope models both achieve a maximum average F1 score of 0.86 among the four datasets. Therefore, One-Class SVM and elliptic envelope are promising algorithms for detecting potential HABs using fluorescence data only.

Identifying biomarkers of serious mental illness, such as altered white matter microstructure, can aid in early diagnosis and treatment. White matter microstructure was assessed using constrained spherical deconvolution of diffusion imaging data in a sample of 219 youth (age 12–25 years, 64.84% female) across 8 sites. Participants were classified as healthy controls (HC; n = 47), familial risk for serious mental illness (n = 31), mild-symptoms (n = 37), attenuated syndromes (n = 66), or discrete disorder (n = 38) based on clinical assessments. Fractional anisotropy (FA) and mean diffusivity (MD) values were derived for the whole brain white matter, forceps minor, anterior cingulate, anterior thalamic radiations (ATR), inferior fronto-occipital fasciculus, superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF). Linear mixed effects models showed a significant effect of age on MD of the left ATR, left SLF, and left UF, and a significant effect of group on FA for all tracts examined. For most tracts, the discrete disorder group had significantly lower FA than other groups, and the attenuated syndromes group had higher FA compared to HC, with few differences between the remaining groups. White matter differences in MDD are most evident in individuals following illness onset, as few significant differences were observed in the risk phase.

Potentially toxic cyanobacteria have been increasingly detected worldwide in water supply systems in recent years. The management of cyanobacteria in source water and through drinking water treatment processes has been a focus of global research for over thirty years. However, despite the volume of research outcomes and the publication of guidance documents, gaps still exist in the knowledge base that inhibits the confident application of individual treatment strategies for the mitigation of aesthetic and health risks associated with cyanobacteria and their metabolites at the full-scale. The main objective of this project is to deliver a suite of tools and other resources to the water industry to support the implementation of a regulatory framework for the management of water quality for the assessment and management of aesthetic and toxicity risks associated with cyanobacteria. This study includes (1) the development of a guide (based on real-world examples) for treatment plant operators to perform plant audits and investigative sampling to assess the risk associated with cyanobacteria in their plants, and validate the performance of existing unit processes, and (2) the validation of a treatment model that can be applied at any plant and used to as a guide to the removals of cyanobacteria and metabolites and the expected quality of treated water under a range of challenges from cyanobacteria. Full-scale sampling was undertaken at three Australian regions in 14 water treatment plants to validate the model. The results presented in this paper represent a comprehensive database of full-scale removal efficiencies of 2-methylisoborneol (MIB) and geosmin for a range of water quality and treatment processes. The major findings and conclusions from this project include: (1) the investigative sampling procedures developed are effective and have been successfully applied by utilities; and (2) while routine monitoring data is important, investigative sampling within the water treatment plant provides more detailed and insightful information about the effectiveness of unit processes within the plant. This paper also identifies the knowledge gaps and needs for further studies.

The increasing frequency and intensity of taste and odour (TO) producing cyanobacteria in water sources is a growing global issue. Geosmin and 2-methylisoborneol (MIB) are the main cyanobacterial TO compounds and can cause complaints from consumers at levels as low as 10 ng/L. However, literature concerning the performance of full-scale treatment processes for geosmin and MIB removal is rare. Hence, the objectives of this study were to: 1) estimate the accumulation and breakthrough of geosmin and MIB inside full-scale water treatment plants; 2) verify the potential impact of sludge recycling practice on performance of plants; and, 3) assess the effectiveness of aged GAC for the removal of these compounds. Sampling after full-scale treatment processes and GAC pilot assays were conducted to achieve these goals. Geosmin and MIB monitoring in full-scale plants provided the opportunity to rank the performance of studied treatment processes with filtration and granular activated carbon providing the best barriers for removal of total and extracellular compounds, correspondingly. Geosmin was removed to a greater extent than MIB using GAC. Geosmin and MIB residuals in water post GAC contactors after two years of operation was 20% and 40% of initial concentrations, correspondingly. Biological activity on the GAC surface enhanced the removal of TO compounds. These observations demonstrated that a multi-barrier treatment approach is required to ensure cyanobacteria and their TO compounds are effectively removed from drinking water.

BACKGROUND AND PURPOSE: Structural connectivity has been thought to be a less sensitive measure of network changes relative to functional connectivity in children with localization-related epilepsy. The aims of this study were to investigate the structural networks in children with localization-related epilepsy and to assess the relation among structural connectivity, intelligence quotient, and clinical parameters. MATERIALS AND METHODS: Forty-five children with nonlesional localization-related epilepsy and 28 healthy controls underwent DTI. Global network (network strength, clustering coefficient, characteristic path length, global efficiency, and small-world parameters), regional network (nodal efficiency), and the network-based statistic were compared between patients and controls and correlated with intelligence quotient and clinical parameters. RESULTS: Patients showed disrupted global network connectivity relative to controls, including reduced network strength, increased characteristic path length and reduced global efficiency, and reduced nodal efficiency in the frontal, temporal, and occipital lobes. Connectivity in multiple subnetworks was reduced in patients, including the frontal-temporal, insula-temporal, temporal-temporal, frontal-occipital, and temporal-occipital lobes. The frontal lobe epilepsy subgroup demonstrated more areas with reduced nodal efficiency and more impaired subnetworks than the temporal lobe epilepsy subgroup. Network parameters were not significantly associated with intelligence quotient, age at seizure onset, or duration of epilepsy. CONCLUSIONS: We found disruption in global and regional networks and subnetworks in children with localization-related epilepsy. Regional efficiency and subnetworks were more impaired in frontal lobe epilepsy than in temporal lobe epilepsy. Future studies are needed to evaluate the implications of disrupted networks for surgical resection and outcomes for specific epileptogenic zones and the relation of disrupted networks to more complex cognitive function.

Purpose To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics. Methods A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting. Results The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~ 15 min) and the code for automated processing has been made publicly available. Conclusions A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs.

ObjectivesTo investigate how young and older people perceive the harms associated with legal and illegal drugs.DesignCross-sectional study: adults aged 18–24 years versus 45+ completed an online survey ranking the perceived harms associated with 11 drugs on 16 drug-related harm criteria.SettingOnline survey.Participants184 participants aged 18–24 years (113 female: mean age 21: SD 1.3) and 91 participants aged 45+ (51 female: mean age 60: SD 8.5).Main outcome measures‘Perception of drug-related harms’: This was measured using a rating scale ranging from 1 (no risk of harm) to 4 (high risk of harm). Participants were also asked about sources which informed their perception on drug-related harms as well as their own personal self-reported drug experiences.ResultsOf the illegal drugs, heroin, methamphetamine and cocaine were rated as the most harmful and cannabis was rated as the least harmful. Alcohol and tobacco were also rated as less harmful. The results showed that perceptions of drug-related harms were inconsistent with current knowledge from research on drugs. Furthermore, perceptions on drug harms were more conservative in the 45+ group for a number of illegal drugs and tobacco. However, the 45+ age group did not perceive alcohol as any more harmful than the younger group.ConclusionsThis survey demonstrates that the greatest misperception was in relation to alcohol-related harms which did not change with age. In order to minimise harms, this misperception needs to be addressed through education and policies that legislate drug use.

Cyanobacteria are a growing concern in the province of Quebec due to recent highly publicised bloom episodes. The health risk associated with the consumption of drinking water coming from contaminated sources was unknown. A study was undertaken to evaluate treatment plants' capacity to treat cyanotoxins below the maximum recommended concentrations of 1.5 μg/L microcystin-LR (MC-LR) and the provisional concentration of 3.7 μg/L anatoxin-a, respectively. The results showed that close to 80% of the water treatment plants are presently able to treat the maximum historical concentration measured in Quebec (5.35 μg/L MC-LR equ.). An increase, due to climate change or other factors, would not represent a serious threat because chlorine, the most popular disinfectant, is effective in treating MC-LR under standard disinfection conditions. The highest concentration of anatoxin-a (2.3 μg/L) measured in natural water thus far in source water is below the current guideline for treated waters. However, higher concentrations of anatoxin-a would represent a significant challenge for the water industry as chlorine is not an efficient treatment option. The use of ozone, potassium permanganate or powder activated carbon would have to be considered.

Background. Chronic kidney disease (CKD) and endstage renal disease (ESRD) are emerging as globally important public health problems, and will necessitate improvements in health-care policy. ESRD incidence/ prevalence data are not available from large parts of the developing world. The main objective of this study is to describe and assess the current clinical practices for patients on maintenance haemodialysis (HD) living in the province of Tehran. Methods. In December 2005, an observational study was performed with 2630 patients (1505 males and 1125 females) from 56 different centres in the province of Tehran (the entire HD population), which has a population of 13.5 million inhabitants. Results. The prevalence/incidence of HD was 194.8/ 77.3 p.m.p. The leading causes of ESRD were diabetes and hypertension. Population of 90.3 and 9% received three and two sessions per week, respectively, with a KT/V mean value of 0.97 0.25. All centres used synthetic membranes, and 68% of the sessions were performed using bicarbonate as a buffer. The type of vascular access was autogenous arteriovenous fistula in 91% of patients. Our findings indicated that compliance with the K/DOQI recommendations for calcium–phosphorus management is difficult to achieve. Only 1.8% of patients achieved all four target laboratory tests. For the management of anaemia, ferritin was the most commonly performed measure of iron status (76.7%). Iron deficiency was seen in

The integration of the 3rd dimension in the production of spatial representation is largely recognized as a valuable approach to comprehend our reality, that is 3D. During the last decade developments in 3D Geoinformation (GI) system have made substantial progress. We are about to have a more complete spatial model and understanding of our planet in different scales. Hence, various communities and cities offer 3D landscape and 3D city models as valuable source and instrument for sustainable management of rural and urban resources. Also municipal utilities, real estate companies benefit from recent developments related to 3D applications. In order to present recent developments and to discuss future trends, academics and practitioners met at the 7th International Workshop on 3D Geoinformation. This book comprises a selection of evaluated, high quality papers that were presented at this workshop in May 2012. The topics focus explicitly on the last achievements (methods, algorithms, models, systems) with respect to 3D GeoInformation requirements. The book is aimed at decision makers and experts as well at students interested in the 3D component of geographical information science including GI engineers, computer scientists, photogrammetrists, land surveyors, urban planners, and mapping specialists.

BACKGROUND AND PURPOSE: Resting-state networks including the DMN have been shown to be abnormal in adults with temporal lobe epilepsy. However, little is known about the DMN in children with medically refractory epilepsy. The aim was to determine whether there was a difference in the DMN in children with medically refractory epilepsy relative to controls. MATERIALS AND METHODS: Eleven children with medically refractory epilepsy and 11 age-matched healthy controls underwent resting-state fMRI. IC analysis was used to identify the DMN. A random-effects analysis was performed on the Z-maps of the DMN within each group and between groups. We calculated the temporal correlation coefficients of pairs of ROIs: PCC/PCUN, mPFC, and left and right lateral parietal cortices. The relations between z scores of temporal correlation coefficients of pairs of ROIs and clinical seizure parameters and IQ were assessed. RESULTS: The patients demonstrated decreased DMN connectivity in the PCC/PCUN, bilateral lateral parietal cortex, and anterior and midcingulate relative to controls. There was reduced connectivity between the mPFC–right lateral parietal cortex, the PCC/PCUN–left lateral parietal cortex, and the PCC/PCUN–right lateral parietal cortex pairs of ROIs in patients compared with controls. There were no significant correlations between the z scores of temporal correlation coefficients of the 6 pairs of ROIs in patients and age of seizure onset, duration of epilepsy, number of medications, seizure frequency, and IQ. CONCLUSIONS: We have found reduced connectivity in the DMN in children with medically refractory epilepsy. Further studies are needed to determine whether different seizure types have different effects on the DMN and whether the impaired connectivity is related to cognitive functions subserved by the DMN.

Background The motivation behind this paper is to aid the automatic phenotyping of mouse embryos, wherein multiple embryos embedded within a single tube were scanned using Magnetic Resonance Imaging (MRI). Results Our algorithm, a modified version of the simplex deformable model of Delingette, addresses various issues with deformable models including initialization and inability to adapt to boundary concavities. In addition, it proposes a novel technique for automatic collision detection of multiple objects which are being segmented simultaneously, hence avoiding major leaks into adjacent neighbouring structures. We address the initialization problem by introducing balloon forces which expand the initial spherical models close to the true boundaries of the embryos. This results in models which are less sensitive to initial minimum of two fold after each stage of deformation. To determine collision during segmentation, our unique collision detection algorithm finds the intersection between binary masks created from the deformed models after every few iterations of the deformation and modifies the segmentation parameters accordingly hence avoiding collision. We have segmented six tubes of three dimensional MR images of multiple mouse embryos using our modified deformable model algorithm. We have then validated the results of the our semi-automatic segmentation versus manual segmentation of the same embryos. Our Validation shows that except paws and tails we have been able to segment the mouse embryos with minor error. Conclusions This paper describes our novel multiple object segmentation technique with collision detection using a modified deformable model algorithm. Further, it presents the results of segmenting magnetic resonance images of up to 32 mouse embryos stacked in one gel filled test tube and creating 32 individual masks.

The purpose of this study was to assess the health service cost of hemodialysis (HD) delivered at hospitals in Iran as a developing country with a well-defined program of renal replacement therapy. A cost analysis was performed from the viewpoint of the 2 hospitals, with 3 shifts and full chairs, on current practice for dialysis maintenance. Cost and patient data were collected in 2006 and from April 1 to May 31, 2007, respectively. A total of 22,464 HD sessions were performed and 247 patients were studied during the study period. The reference year for the value of USD for different mentioned costs was 2006. Health care sector costs associated with each HD session were estimated at US$78.87. Most of the total maintenance expenditure was made up of medical supplies (36.19%), with dialyzers as the major cost driver. Staff salaries represented 17% of the cost and fixed direct capital costs accounted for 21.4%. Of the family members, 32.4% accompanied their patients. The mean cost for transportation of patients and accompanied person was US$3.15 +/- 2.83 and US$1.5 +/- 0.29, respectively. These findings are important in the light of limited available resources coupled with the increasing prevalence of kidney failure. A major attempt should also be made to increase peritoneal dialysis coverage as in some centers we cannot keep all chairs full, especially in some vast areas. It is highly recommended to place initial focus on strategies and treatments that slow disease progression, to postpone renal replacement therapy to save resources.

Cyanobacteria and their taste and odor (T&O) compounds are a growing concern in water sources globally. Geosmin and 2-methylisoborneol (MIB) are the most commonly detected T&O compounds associated with cyanobacterial presence in drinking water sources. The use of ultraviolet and hydrogen peroxide (H2O2) as an advanced oxidation treatment for T&O control is an emerging technology. However, residual H2O2 (>80% of the initial dose) has to be removed from water prior final disinfection. Recently, granular activated carbon (GAC) is used to remove H2O2 residual. The objective of this study is to assess the impact of H2O2 quenching and aging processes on GAC capacity for the removal of geosmin and MIB. Pilot columns with different types of GAC and presence/absence of H2O2 have been used for this study. H2O2 removal for the operational period of 6 months has no significant impact on GAC capacity to remove the geosmin and MIB from water.