Search

A computational tool to automatically analyze the Raman spectral features of nanomaterials. Codes and experimental data.

Wetland bird species have been declining in population size worldwide as climate warming and land-use change affect their suitable habitats. We used species distribution models (SDMs) to predict changes in range dynamics for 64 non-passerine wetland birds breeding in Europe, including range size, position of centroid, and margins. We fitted the SDMs with data collected for the first European Breeding Bird Atlas (EBBA1) and climate and land-use data to predict distributional changes over a century (the 1970s–2070s). The predicted annual changes were then compared to observed annual changes in range size and range centroid over a time period of 30 years using data from the second European Breeding Bird Atlas (EBBA2). Our models successfully predicted ca. 75% of the 64 bird species to contract their breeding range in the future, while the remaining species (mostly southerly breeding species) were predicted to expand their breeding ranges northward. The northern margins of southerly species and southern margins of northerly species, both, predicted to shift northward. Predicted changes in range size and shifts in range centroids were broadly positively associated with the observed changes, although some species deviated markedly from the predictions. The predicted average shift in core distributions was ca. 5 km/year towards the north (5% Northeast, 45% North, and 40% Northwest), compared to a slower observed average shift of ca. 3.9 km/year. Predicted changes in range centroids were generally larger than observed changes, which suggests that bird distribution changes may lag behind environmental changes leading to "climate debt. We suggest that predictions of SDMs should be viewed as qualitative rather than quantitative outcomes, indicating that care should be taken concerning single species. Still, our results highlight the urgent need for management actions such as wetland creation and restoration to improve wetland birds' resilience to the expected environmental cha

Background There is now evolving data exploring the relationship between depression and various individual lifestyle factors such as diet, physical activity, sleep, alcohol intake, and tobacco smoking. While this data is compelling, there is a paucity of longitudinal research examining how multiple lifestyle factors relate to depressed mood, and how these relations may differ in individuals with major depressive disorder (MDD) and those without a depressive disorder, as ‘healthy controls’ (HC). Methods To this end, we assessed the relationships between 6 key lifestyle factors (measured via self-report) and depressed mood (measured via a relevant item from the Patient Health Questionnaire) in individuals with a history of or current MDD and healthy controls (HCs). Cross-sectional analyses were performed in the UK Biobank baseline sample, and longitudinal analyses were conducted in those who completed the Mental Health Follow-up. Results Cross-sectional analysis of 84,860 participants showed that in both MDD and HCs, physical activity, healthy diet, and optimal sleep duration were associated with less frequency of depressed mood (all p < 0.001; ORs 0.62 to 0.94), whereas screen time and also tobacco smoking were associated with higher frequency of depressed mood (both p < 0.0001; ORs 1.09 to 1.36). In the longitudinal analysis, the lifestyle factors which were protective of depressed mood in both MDD and HCs were optimal sleep duration (MDD OR = 1.10; p < 0.001, HC OR = 1.08; p < 0.001) and lower screen time (MDD OR = 0.71; p < 0.001, HC OR = 0.80; p < 0.001). There was also a significant interaction between healthy diet and MDD status (p = 0.024), while a better-quality diet was indicated to be protective of depressed mood in HCs (OR = 0.92; p&thi

The unprecedented scale of testing required to effectively control the coronavirus disease (COVID-19) pandemic has necessitated urgent implementation of rapid testing in clinical microbiology laboratories. To date, there are limited data available on the analytical performance of emerging commercially available assays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and integration of these assays into laboratory workflows. Here, we performed a prospective validation study of a commercially available assay, the AusDiagnostics Coronavirus Typing (8-well) assay. Respiratory tract samples for SARS-CoV-2 testing were collected between 1 March and 25 March 2020. All positive samples and a random subset of negative samples were sent to a reference laboratory for confirmation. In total, 2673 samples were analysed using the Coronavirus Typing assay. The predominant sample type was a combined nasopharyngeal/throat swab (2640/2673; 98.8%). Fifty-four patients were positive for SARS-CoV-2 (2.0%) using the Coronavirus Typing assay; 53/54 (98.1%) positive results and 621/621 (100%) negative results were concordant with the reference laboratory. Compared to the reference laboratory gold standard, sensitivity of the Coronavirus Typing assay for SARS-CoV-2 was 100% (95% CI 93.2-100%), specificity 99.8% (95% CI 99.1-100%), positive predictive value 98.1% (95% CI 90.2-99.7%) and negative predictive value 100% (95% CI 99.4-100%). In many countries, standard regulatory requirements for the introduction of new assays have been replaced by emergency authorisations and it is critical that laboratories share their post-market validation experiences, as the consequences of widespread introduction of a suboptimal assay for SARS-CoV-2 are profound. Here, we share our in-field experience, and encourage other laboratories to follow suit.

There is increasing academic and clinical interest in how "lifestyle factors" traditionally associated with physical health may also relate to mental health and psychological well-being. In response, international and national health bodies are producing guidelines to address health behaviors in the prevention and treatment of mental illness. However, the current evidence for the causal role of lifestyle factors in the onset and prognosis of mental disorders is unclear. We performed a systematic meta-review of the top-tier evidence examining how physical activity, sleep, dietary patterns and tobacco smoking impact on the risk and treatment outcomes across a range of mental disorders. Results from 29 meta-analyses of prospective/cohort studies, 12 Mendelian randomization studies, two meta-reviews, and two meta-analyses of randomized controlled trials were synthesized to generate overviews of the evidence for targeting each of the specific lifestyle factors in the prevention and treatment of depression, anxiety and stress-related disorders, schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder. Standout findings include: a) convergent evidence indicating the use of physical activity in primary prevention and clinical treatment across a spectrum of mental disorders; b) emerging evidence implicating tobacco smoking as a causal factor in onset of both common and severe mental illness; c) the need to clearly establish causal relations between dietary patterns and risk of mental illness, and how diet should be best addressed within mental health care; and d) poor sleep as a risk factor for mental illness, although with further research required to understand the complex, bidirectional relations and the benefits of non-pharmacological sleep-focused interventions. The potentially shared neurobiological pathways between multiple lifestyle factors and mental health are discussed, along with directions for future research, and recommendations for the imp

BACKGROUND: There is now evolving data exploring the relationship between depression and various individual lifestyle factors such as diet, physical activity, sleep, alcohol intake, and tobacco smoking. While this data is compelling, there is a paucity of longitudinal research examining how multiple lifestyle factors relate to depressed mood, and how these relations may differ in individuals with major depressive disorder (MDD) and those without a depressive disorder, as 'healthy controls' (HC). METHODS: To this end, we assessed the relationships between 6 key lifestyle factors (measured via self-report) and depressed mood (measured via a relevant item from the Patient Health Questionnaire) in individuals with a history of or current MDD and healthy controls (HCs). Cross-sectional analyses were performed in the UK Biobank baseline sample, and longitudinal analyses were conducted in those who completed the Mental Health Follow-up. RESULTS: Cross-sectional analysis of 84,860 participants showed that in both MDD and HCs, physical activity, healthy diet, and optimal sleep duration were associated with less frequency of depressed mood (all p < 0.001; ORs 0.62 to 0.94), whereas screen time and also tobacco smoking were associated with higher frequency of depressed mood (both p < 0.0001; ORs 1.09 to 1.36). In the longitudinal analysis, the lifestyle factors which were protective of depressed mood in both MDD and HCs were optimal sleep duration (MDD OR = 1.10; p < 0.001, HC OR = 1.08; p < 0.001) and lower screen time (MDD OR = 0.71; p < 0.001, HC OR = 0.80; p < 0.001). There was also a significant interaction between healthy diet and MDD status (p = 0.024), while a better-quality diet was indicated to be protective of depressed mood in HCs (OR = 0.92; p = 0.045) but was not associated with depressed mood in the MDD sample. In a cross-sectional (OR = 0.91; p < 0.0001) analysis, higher frequency of alcohol consumption was surprisingly associated with reduced frequency of depr