Species specific responses to stressors hamper Trichoptera recovery.

Worldwide, aquatic biodiversity is severely threatened as a result of anthropogenic pressures such as pollution, habitat destruction and climate change. Widescale legislation resulted in reduced nutrient- and pesticide loads, and restoration measures allowed modest recovery of freshwater biodiversity. However, from 2010 onwards, recovery in the otherwise unrestored aquatic habitats stagnated. The aim of the present study was therefore to reveal long-term trends in aquatic biodiversity in an anthropogenic landscape and to explain the observed patterns. To this end, over 40 years of biomonitoring data of the indicative taxa group Trichoptera (caddisflies), with an exceptionally high spatial and temporal resolution, was employed. Periods of recovery, stagnation, and decline were delineated using linear and non-linear modelling approaches. Subsequently, species were grouped based on abundance patterns over time and this grouping was used to ascertain species-specific responses to anthropogenic stressors using a trait-based approach. Richness and abundance of all Trichoptera jointly, as well as of the five most abundant and the remaining 136 species, significantly increased from 1980 to significant breakpoints from 2010 onwards, after which these metrics, except the abundances of the 5 most abundant, declined significantly. Trend-based species groupings were not significantly explained by biological traits or ecological preferences. However, Trichoptera species increasing in abundance were less sensitive to climate change and poor water quality, or concerned sensitive species which benefited from restoration measures. Species with stable or declining abundances showed higher sensitivity to climate change. The Trichoptera declining in abundance indicated that conditions in non-protected or restored habitats did not improve due to climate change on top of the other anthropogenic pressures. These observations reinforce the need for increased efforts to improve the only moderately restored water- and habitat quality in anthropogenic landscapes to halt further aquatic ecosystem degradation and to turn biodiversity losses again into recoveries.

Assessing post-fire water quality changes in reservoirs: Insights from a large dataset in Portugal.

Wildfires in the Mediterranean basin, especially in Portugal, have increased in extent and frequency over the last few years. One of the impacts of wildfires on humans and ecosystems is on the water quality of surface waters. Ashes and increased erosion rates might, for example, change oxygen levels and elevate the influx of sediments, nutrients, or other water quality-related components like metals and polycyclic aromatic hydrocarbons (PAHs), possibly affecting water supply. In this study, time series of eight water quality parameters: biological- and chemical oxygen demand (BOD and COD), electrical conductivity (EC), total phosphorous (TP), nitrate (NO3-), total suspended sediments (TSS), dissolved oxygen (DO), and pH, were assessed via changepoint analysis to identify events of post-fire water contamination in over 60 Portuguese reservoirs. Further, possible fire, watershed, reservoir, and climate-related drivers were linked with the occurrence of these contamination events through logistic regression using generalized additive models. All measured parameters exhibited post-fire changes, with some being more frequently affected than others. The concentrations of TP, NO3-, and TSS showed a noticeable increase following 9.6 %, 12.6 %, and 13.6 % of all wildfires, respectively. Most changes fell into the unusually large fire seasons of 2003-2005 and 2017. The most significant impacts could be seen in southern reservoirs after the fire seasons of 2003-2005. The burned area ratio of the watershed was identified as the main driver of post-fire water contamination, while reservoir and climate-related characteristics like water levels also played a significant role in some parameters. Increased levels of suspended sediments were identified as a potential threat to water supply, especially when large wildfires coincide with drought-induced low reservoir water levels. The identification of post-fire water contamination events and their drivers from large datasets can inform water managers about potential threats to water supply.

Understanding the Effects of COVID-19 on Child Maltreatment Reporting Among Rural Versus Urban Communities in the United States.

The onset of the coronavirus disease 2019 (COVID-19) pandemic impacted child protective services (CPS) reporting systems in the United States. It may have also led to widened gaps between rural and urban communities in child maltreatment (CM) report rates due to decreased interaction between children and mandated reporters especially in urban jurisdictions. Using data from the National Child Abuse and Neglect Data System, this study tests the hypothesis that during the onset of the COVID-19 pandemic, the decrease in CM reports made to CPS in urban counties was more pronounced than in rural counties. Reports of CM received by CPS offices between January 6, 2020 and June 28, 2020 were aggregated to per-county-per-week-per-10,000 children maltreatment report rates. We used changepoint analyses to analyze the inter- and intra-region incidence rate ratios among rural and urban counties. Moreover, we used multilevel random effects models to generate regression coefficients for the associations between rates of children with a maltreatment report, COVID-19 occurrence, rural-urban designation, and maltreatment risk factors. During the study period, rates of children with a maltreatment report among urban counties decreased more dramatically when compared with rural counties. Our findings persisted even with the inclusion of control variables associated with maltreatment risk factors. Social distancing restrictions may have had the unintended consequence of decreasing the visibility of at-risk children in urban counties more so than in rural counties. Considering geography is critical to continue to protect children during the COVID-19 pandemic and as we prepare for future disasters.

The Changing Trend of Paediatric Emergency Department Visits in Malaysia Following the COVID-19 Pandemic.

Background The coronavirus disease 2019 (COVID-19) pandemic has impacted the emergency department (ED) due to the surge in medical demand and changes in the characteristics of paediatric visits. Additionally, the trend for paediatric ED visits has decreased globally, secondary to implementing lockdowns to stop the spread of COVID-19. We aim to study the trend and characteristics of paediatric ED visits following Malaysia's primary timeline of the COVID-19 pandemic. Methods and materials A five-year time series observational study of paediatric ED patients from two tertiary hospitals in Malaysia was conducted from March 17, 2017 (week 11 2017) to March 17, 2022 (week 12 2022). Aggregated weekly data were analysed using R statistical software version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria) against significant events during the COVID-19 pandemic to detect influential changepoints in the trend. The data collected were the number of ED visits, triage severity, visit outcomes and ED discharge diagnosis. Results Overall, 175,737 paediatric ED visits were recorded with a median age of three years and predominantly males (56.8%). A 57.57% (p<0.00) reduction in the average weekly ED visits was observed during the Movement Control Order (MCO) period. Despite the increase in the proportion of urgent (odds ratio (OR): 1.23, p<0.00) and emergent or life-threatening (OR: 1.79, p<0.00) cases, the proportion of admissions decreased. Whilst the changepoints during the MCO indicated a rise in respiratory, fever or other infectious diseases, or gastrointestinal conditions, diagnosis of complications originating from the perinatal period declined from July 19, 2021 (week 29 2021). Conclusion The incongruent change in disease severity and hospital admission reflects the potential effects of the healthcare system reform and socioeconomic impact as the pandemic evolves. Future studies on parental motivation to seek emergency medical attention may provide insight into the timing and choice of healthcare service utilisation.

Body temperature patterns during pregnancy and parturition in moose.

Gestation and lactation have high energetic requirements. Up to three-fourths of the gestation period in moose (Alces alces) overlaps with the food-scarce period in winter. During this period, moose deal with the limited forage resources available through hypometabolism with decreased heart rate and body temperature (Tb). Body temperature is also an indicator of oestrus, pregnancy and parturition, which is well documented in several domestic species. In this study, we sought to determine if moose displayed a similar Tb pattern during pregnancy and parturition to domesticated ruminants, and if we could detect parturition by combining Tb and activity data. We studied the Tb pattern of 30 free-ranging adult female moose (≥1.5 years old), equipped with ruminal temperature loggers and GPS collars. We documented a 0.13-0.19°C higher Tb in pregnant compared to non-pregnant moose, depending on the study area with the Tb difference increasing along a south-north gradient, and a drop in Tb and in activity when parturition was imminent. Detection of parturition was highly successful when combining Tb and activity data with an accuracy of 91.5%. Our findings demonstrate that Tb responses to pregnancy and parturition in a wild capital-breeding ruminant are similar to those of domesticated ruminants.

Katdetectr: an R/bioconductor package utilizing unsupervised changepoint analysis for robust kataegis detection.

BACKGROUND: Kataegis refers to the occurrence of regional genomic hypermutation in cancer and is a phenomenon that has been observed in a wide range of malignancies. A kataegis locus constitutes a genomic region with a high mutation rate (i.e., a higher frequency of closely interspersed somatic variants than the overall mutational background). It has been shown that kataegis is of biological significance and possibly clinically relevant. Therefore, an accurate and robust workflow for kataegis detection is paramount. FINDINGS: Here we present Katdetectr, an open-source R/Bioconductor-based package for the robust yet flexible and fast detection of kataegis loci in genomic data. In addition, Katdetectr houses functionalities to characterize and visualize kataegis and provides results in a standardized format useful for subsequent analysis. In brief, Katdetectr imports industry-standard formats (MAF, VCF, and VRanges), determines the intermutation distance of the genomic variants, and performs unsupervised changepoint analysis utilizing the Pruned Exact Linear Time search algorithm followed by kataegis calling according to user-defined parameters.We used synthetic data and an a priori labeled pan-cancer dataset of whole-genome sequenced malignancies for the performance evaluation of Katdetectr and 5 publicly available kataegis detection packages. Our performance evaluation shows that Katdetectr is robust regarding tumor mutational burden and shows the fastest mean computation time. Additionally, Katdetectr reveals the highest accuracy (0.99, 0.99) and normalized Matthews correlation coefficient (0.98, 0.92) of all evaluated tools for both datasets. CONCLUSIONS: Katdetectr is a robust workflow for the detection, characterization, and visualization of kataegis and is available on Bioconductor: https://doi.org/doi:10.18129/B9.bioc.katdetectr.

Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits.

Including all available data when developing equations to relate midinfrared spectra to a phenotype may be suboptimal for poorly represented spectra. Here, an alternative local changepoint approach was developed to predict six milk technological traits from midinfrared spectra. Neighbours were objectively identified for each predictand as those most similar to the predictand using the Mahalanobis distances between the spectral principal components, and subsequently used in partial least square regression (PLSR) analyses. The performance of the local changepoint approach was compared to that of PLSR using all spectra (global PLSR) and another LOCAL approach, whereby a fixed number of neighbours was used in the prediction according to the correlation between the predictand and the available spectra. Global PLSR had the lowest RMSEV for five traits. The local changepoint approach had the lowest RMSEV for one trait; however, it outperformed the LOCAL approach for four traits. When the 5% of the spectra with the greatest Mahalanobis distance from the centre of the global principal component space were analysed, the local changepoint approach outperformed the global PLSR and the LOCAL approach in two and five traits, respectively. The objective selection of neighbours improved the prediction performance compared to utilising a fixed number of neighbours; however, it generally did not outperform the global PLSR.

Research and Exploratory Analysis Driven-Time-data Visualization (read-tv) software.

MOTIVATION: Research & Exploratory Analysis Driven Time-data Visualization (read-tv) is an open source R Shiny application for visualizing irregularly and regularly spaced longitudinal data. read-tv provides unique filtering and changepoint analysis (CPA) features. The need for these analyses was motivated by research of surgical work-flow disruptions in operating room settings. Specifically, for the analysis of the causes and characteristics of periods of high disruption-rates, which are associated with adverse surgical outcomes. MATERIALS AND METHODS: read-tv is a graphical application, and the main component of a package of the same name. read-tv generates and evaluates code to filter and visualize data. Users can view the visualization code from within the application, which facilitates reproducibility. The data input requirements are simple, a table with a time column with no missing values. The input can either be in the form of a file, or an in-memory dataframe- which is effective for rapid visualization during curation. RESULTS: We used read-tv to automatically detect surgical disruption cascades. We found that the most common disruption type during a cascade was training, followed by equipment. DISCUSSION: read-tv fills a need for visualization software of surgical disruptions and other longitudinal data. Every visualization is reproducible, the exact source code that read-tv executes to create a visualization is available from within the application. read-tv is generalizable, it can plot any tabular dataset given the simple requirements that there is a numeric, datetime, or datetime string column with no missing values. Finally, the tab-based architecture of read-tv is easily extensible, it is relatively simple to add new functionality by implementing a tab in the source code. CONCLUSION: read-tv enables quick identification of patterns through customizable longitudinal plots; faceting; CPA; and user-specified filters. The package is available on GitHub under an MIT license.

The Role of Uniform Meropenem Usage in Acinetobacter baumannii Clone Replacement.

The dominant carbapenem resistant Acinetobacter baumannii harboring blaOXA-23-like carbapenemase was replaced by blaOXA-40-like carriers in a Hungarian tertiary-care center with high meropenem but relatively low imipenem use. We hypothesized that alterations in antibiotic consumption may have contributed to this switch. Our workgroup previous study examined the relation between resistance spiral and the antibiotic consumption, and the results suggest that the antibiotic usage provoked the increasing resistance in case of A. baumannii. We aimed at measuring the activity of imipenem and meropenem to compare the selection pressure exerted by the different carbapenems in time-kill assays. Strain replacement was confirmed by whole genome sequencing, core-genome multilocus sequence typing (cgMLST), and resistome analysis. Based on results of the time-kill assays, we found a significant difference between two different sequence-types (STs) in case of meropenem, but not in case of imipenem susceptibility. The newly emerged ST636 and ST492 had increased resistance level against meropenem compared to the previously dominant ST2 and ST49. On the other hand, the imipenem and colistin resistance profiles were similar. These results suggest, that the uniform meropenem usage may have contributed to A. baumannii strain replacement in our setting.

Substituting values for censored data from Texas, USA, reservoirs inflated and obscured trends in analyses commonly used for water quality target development.

We calculated four median datasets (chlorophyll a, Chl a; total phosphorus, TP; and transparency) using multiple approaches to handling censored observations, including substituting fractions of the quantification limit (QL; dataset 1 = 1QL, dataset 2 = 0.5QL) and statistical methods for censored datasets (datasets 3-4) for approximately 100 Texas, USA reservoirs. Trend analyses of differences between dataset 1 and 3 medians indicated percent difference increased linearly above thresholds in percent censored data (%Cen). This relationship was extrapolated to estimate medians for site-parameter combinations with %Cen > 80%, which were combined with dataset 3 as dataset 4. Changepoint analysis of Chl a- and transparency-TP relationships indicated threshold differences up to 50% between datasets. Recursive analysis identified secondary thresholds in dataset 4. Threshold differences show that information introduced via substitution or missing due to limitations of statistical methods biased values, underestimated error, and inflated the strength of TP thresholds identified in datasets 1-3. Analysis of covariance identified differences in linear regression models relating transparency-TP between datasets 1, 2, and the more statistically robust datasets 3-4. Study findings identify high-risk scenarios for biased analytical outcomes when using substitution. These include high probability of median overestimation when %Cen > 50-60% for a single QL, or when %Cen is as low 16% for multiple QL's. Changepoint analysis was uniquely vulnerable to substitution effects when using medians from sites with %Cen > 50%. Linear regression analysis was less sensitive to substitution and missing data effects, but differences in model parameters for transparency cannot be discounted and could be magnified by log-transformation of the variables.