Tools for bioindicator assessment in rivers: The importance of spatial scale,land use patterns and biotic integration

This study outlines an alternative, versatile and flexible procedure to the Assessment System for the Ecological Quality of Streams and Rivers throughout Europe using Benthic Macroinvertebrates (AQEM) protocol for selecting and assessing candidate bioindicators for Water Framework Directive (WFD) compliant monitoring programmes. Based on different forms of benthic macroinvertebrate data using relative abundance of family level taxonomic groups, metrics and traits collected during spring 2010 at 96 lotic sites across northern Portugal, the procedure employs components of top down and bottom up analytical processes and introduces the concept of niche breadth to assess biological quality element response to environmental and stressor parameters across different spatial levels. Random Forest classification revealed that fractal and non-fractal land use metrics at basin and local level were extremely important determinants of Water Framework Directive determination of “Good” ecological quality, particularly at the local scale. The amount of urbanization at the lower spatial level was a particularly important determinant of ecological quality, while the extent and type of forest (especially coniferous) was more important at higher, river basin scale. Distance-based linear models (DISTLM) and distance-based redundancy analysis (dbRDA) were used to determine associations between invertebrate data and non-redundant environmental predictors selected using the Akaike Information Criterion (AIC). Results revealed a stronger association between invertebrate traits and selected environmental predictors compared to the other types of invertebrate data, although some association between invertebrate relative abundance and eutrophication was detected. Principal Components Analyses (PCA) were run for the non-redundant sets of predictors for each macroinvertebrate data set to extract an environmental quality gradient along the first axis. Niche breadth, calculated for candidate indicators to avoid bias resulting from expert judgement, was distributed by rank along its respective PCA gradient. Five candidate indicators for each data type were selected for their preference for the most pristine sites and five were selected due to their close link with the most degraded streams. Candidate bioindicators for impacted sites tended to be stenobiotic in character, due to the impoverished structural and functional diversity associated with such conditions. Finally Partial Least Squares Regression was used to refine and validate selected candidate metrics, to produce a comprehensive final list of macroinvertebrate measures of ecological quality.     

Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ₁-type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ₂-norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.     

Fasting blood glucose as a predictor of mortality: Lost in translation

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Robustness of Shrunken Predictors in Stratified Populations

The effect of assuming an inadequate superpopulation model in stratified sampling is studied when a shrinkage technique is used. The robustness is close related with the balance of the strata samples. This theoretical problem is present in studies of ecology. The results obtained enable to estimate the abundance of plankton when salinity is the auxiliary variable and expert's advise, in coast experimental stations networks, is used for fixing the superpopulation model.     

Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models

While biological distributions are not static and change/evolve through space and time, nonstationarity of climatic and land‐use conditions is frequently neglected in species distribution models. Even recent techniques accounting for spatiotemporal variation of species occurrence basically consider the environmental predictors as static; specifically, in most studies using species distribution models, predictor values are averaged over a 50‐ or 30‐year time period. This could lead to a strong bias due to monthly/annual variation between the climatic conditions in which species' locations were recorded and those used to develop species distribution models or even a complete mismatch if locations have been recorded more recently. Moreover, the impact of land‐use change has only recently begun to be fully explored in species distribution models, but again without considering year‐specific values. Excluding dynamic climate and land‐use predictors could provide misleading estimation of species distribution. In recent years, however, open‐access spatially explicit databases that provide high‐resolution monthly and annual variation in climate (for the period 1901–2016) and land‐use (for the period 1992–2015) conditions at a global scale have become available. Combining species locations collected in a given month of a given year with the relative climatic and land‐use predictors derived from these datasets would thus lead to the development of true dynamic species distribution models (D‐SDMs), improving predictive accuracy and avoiding mismatch between species locations and predictor variables. Thus, we strongly encourage modelers to develop D‐SDMs using month‐ and year‐specific climatic data as well as year‐specific land‐use data that match the period in which species data were collected.     

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up-to-date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products.     

Early Predictors of Gestational Diabetes Mellitus in IVF-Conceived Pregnancies

ObjectiveGestational diabetes mellitus (GDM) is associated with adverse maternal and fetal outcomes. This study aimed to identify early and reliable GDM predictors that would enable implementation of preventive and management measures.MethodsThe participants were a 28-week prospective cohort of in vitro fertilization (IVF)-conceived pregnant women (≤39 years, body mass index [BMI] 18.5-38 kg/m²) without a known history of diabetes mellitus. Fasting blood samples were analyzed at baseline (pre-IVF) and 12 weeks’ gestation for reproductive hormones, glucose, serum insulin, lipids, thyroid function, adiponectin, and lipopolysaccharide-binding protein. At 28 weeks, a 75-g oral glucose tolerance test was used to screen for GDM.ResultsFor the overall group at baseline, 22% had BMI ≥30 kg/m², 45% had polycystic ovary syndrome, 16% had hemoglobin A1C of 5.7% to 6.1%, and 14% had a past history of GDM. At 28 weeks of gestation (n = 158), 34 women had developed GDM and 124 had not. Significant baseline predictors of GDM onset included greater BMI (29.0 vs 25.8 kg/m²), older age (34 vs 32 years), higher levels of follicle-stimulating hormone/luteinizing hormone ratio (1.2 vs 1.0), hemoglobin A1C (5.5 vs 5.2%), insulin (10.6 vs 7.1 μIU/mL), homeostatic model assessment of insulin resistance (2.2 vs 1.7), total cholesterol (199 vs 171 mg/dL), and low-density lipoprotein cholesterol (123 vs 105 mg/dL), and lower triglyceride levels (74 vs 76 mg/dL). Significant 12-week GDM predictors included greater maternal weight gain (delta: 3.4 vs 1.5 kg) and higher levels of insulin (11.3 vs 7.6 μIU/mL), triglycerides (178 vs 120 mg/dL), and homeostatic model assessment of insulin resistance (2.3 vs 1.5). Twelve-week BMI is a predictor of GDM following adjustment for polycystic ovary syndrome status and maternal age.ConclusionWhile preconception maternal BMI, age, and follicle-stimulating hormone/luteinizing hormone ratio are predictors of subsequent development of GDM, early IVF-conceived gestational weight gain is the best predictor of GDM onset.     

20-year trajectories of positive and negative symptoms after the first psychotic episode in patients with schizophrenia spectrum disorder: results from the OPUS study

This study aimed to identify the 20-year trajectories of positive and negative symptoms after the first psychotic episode in a sample of patients with an ICD-10 diagnosis of schizophrenia spectrum disorder, and to investigate the baseline characteristics and long-term outcomes associated with these trajectories. A total of 373 participants in the OPUS trial were included in the study. Symptoms were assessed at baseline and after 1, 2, 5, 10 and 20 years using the Scales for the Assessment of Positive and Negative Symptoms. We used latent class growth mixture modelling to identify trajectories, and multinominal regression analyses to investigate predictors of membership to identified trajectories. Five trajectories of positive symptoms were identified: early continuous remission (50.9% of the sample), stable improvement (18.0%), intermittent symptoms (10.2%), relapse with moderate symptoms (11.9%), and continuous severe symptoms (9.1%). Substance use disorder (odds ratio, OR: 2.83, 95% CI: 1.09-7.38, p=0.033), longer duration of untreated psychosis (OR: 1.02, 95% CI: 1.00-1.03, p=0.007) and higher level of negative symptoms (OR: 1.60, 95% CI: 1.07-2.39, p=0.021) were predictors of the relapse with moderate symptoms trajectory, while only longer duration of untreated psychosis (OR: 1.01, 95% CI: 1.00-1.02, p=0.030) predicted membership to the continuous severe symptoms trajectory. Two trajectories of negative symptoms were identified: symptom remission (51.0%) and continuous symptoms (49.0%). Predictors of the continuous symptoms trajectory were male sex (OR: 3.03, 95% CI: 1.48-6.02, p=0.002) and longer duration of untreated psychosis (OR: 1.01, 95% CI: 1.00-1.02, p=0.034). Trajectories displaying continuous positive and negative symptoms were linked to lower neurocognition, as measured by the Brief Assessment of Cognition in Schizophrenia (BACS) (z-score: –0.78, CI: –1.39 to –0.17, for continuous positive symptoms; z-score: –0.33, CI: –0.53 to –0.13, for continuous negative symptoms). The same trajectories were also linked to higher use of antipsychotic medication at 20-year follow-up (continuous positive symptoms: 78%; continuous negative symptoms: 67%). These findings suggest that the majority of patients with first-episode schizophrenia spectrum disorder have a trajectory with early stable remission of positive symptoms. Long duration of untreated psychosis and comorbid substance abuse are modifiable predictors of poor trajectories for positive symptoms in these patients. In about half of patients, negative symptoms do not improve over time. These symptoms, in addition to being associated with poor social and neurocognitive functioning, may prevent patients from seeking help.     

Circulating cell-free DNA,peripheral lymphocyte subsets alterations and neutrophil lymphocyte ratio in assessment of COVID-19 severity

Cell destruction results in plasma accumulation of cell-free DNA (cfDNA). Dynamic changes in circulating lymphocytes are features of COVID-19. We aimed to investigate if cfDNA level can serve in stratification of COVID-19 patients, and if cfDNA level is associated with alterations in lymphocyte subsets and neutrophil-to-lymphocyte ratio (NLR). This cross-sectional comparative study enrolled 64 SARS-CoV-2-positive patients. Patients were subdivided to severe and non-severe groups. Plasma cfDNA concentration was determined by real-time quantitative PCR. Lymphocyte subsets were assessed by flow cytometry. There was significant increase in cfDNA among severe cases when compared with non-severe cases. cfDNA showed positive correlation with NLR and inverse correlation with T cell percentage. cfDNA positively correlated with ferritin and C-reactive protein. The output data of performed ROC curves to differentiate severe from non-severe cases revealed that cfDNA at cut-off ≥17.31 ng/µl and AUC of 0.96 yielded (93%) sensitivity and (73%) specificity. In summary, excessive release of cfDNA can serve as sensitive COVID-19 severity predictor. There is an association between cfDNA up-regulation and NLR up-regulation and T cell percentage down-regulation. cfDNA level can be used in stratification and personalized monitoring strategies in COVID-19 patients.