Competition from non-native salmonids is potentially impairing efforts to restore Atlantic salmon (Salmo salar) to parts of their historical range. In three separate meta-analyses, we collected 104 ...effect sizes from 25 published papers to quantify the effect of both native and non-native salmonids on the performance (i.e., behaviour, habitat use, growth and survival) of Atlantic salmon. The presence of other species had negative effects on the performance of Atlantic salmon; in particular, non-native species and brown trout (Salmo trutta), whether native or non-native, had the most negative effects. Contrary to our predictions, the negative effects of other species were not exacerbated in laboratory compared to field studies and did not increase with total salmonid abundance or the relative body size of the competitors. However, most studies in our analyses were conducted under laboratory conditions and at densities much higher than found in nature. Thus, a realistic assessment of the potential success of restoration programs when interspecific competitors are present should include more studies conducted under natural conditions.
Habitat degradation is one of the major reasons for freshwater species decline. Hydrogeomorphological processes (such as sediment transport, bank erosion, and flooding) operate at the catchment scale ...and determine habitat features in river reaches. However, habitat quality indices and restoration for freshwater fish species are often implemented at small spatial scales of a few hundred metres. The Morphological Quality Index (MQI) considers fluvial processes at larger scales as well as channel forms, human impacts, and historical changes, but few studies have assessed its relevance for ecosystem health. We investigated relationships between the MQI, habitat quality (using the Qualitative Habitat Evaluation Index, QHEI), land cover, and fish metrics (number of fish species, index of biotic integrity (IBI), and trout biomass) in 26 salmonid streams in Aotearoa New Zealand and Southern Ontario, Canada. We found a significant correlation between the MQI and QHEI, and both metrics were correlated with urban and native forest proportion in the catchment. However, we found no relation between the MQI and the proportion of agricultural land in the catchment, while the QHEI was correlated with agricultural land in the riparian zone, highlighting the importance of vegetated riparian buffers in providing fish habitat. Establishing a strong correlation with fish metrics remains challenging. Nevertheless, a modified MQI targeting ecological health could be used as an effective management tool for aquatic conservation.
The prerequisite of therapeutic drug design and discovery is to identify novel molecules and developing lead candidates with desired biophysical and biochemical properties. Deep generative models ...have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to generate new molecules with desired target properties is by constraining the critical fucntional groups or the core scaffolds in the generation process. To this end, we developed a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of the desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We demonstrated that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific data sets, we generate covalent and noncovalent antiviral inhibitors targeting viral proteins. To measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease (Mpro) and nonstructural protein endoribonuclease (NSP15) targets. Most importantly, our deep learning model performs well with relatively small 3D structural training data and quickly learns to generalize to new scaffolds, highlighting its potential application to other domains for generating target specific candidates.
Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and ...disease. Here we sought to further establish the credentials of ‘brain-predicted age’ as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data.
Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data.
CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error MAE = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient ICC = 0.90–0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83–0.96) and poor-moderate levels for WM and raw data (0.51–0.77).
Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.
•Chronological age can be accurately predicted using convolutional neural networks.•Age predicted is accurate even using raw structural neuroimaging data.•Brain-predicted age can be generated in a clinically applicable timeframe.•Brain-predicted age is significantly heritable.•Brain-predicted age is highly reliable, both within and between scanners.
The cultural transmission of technical know-how has proven vital to the success of our species. The broad diversity of learning contexts and social configurations, as well as the various kinds of ...coordinated interactions they involve, speaks to our capacity to flexibly adapt to and succeed in transmitting vital knowledge in various learning contexts. Although often recognized by ethnographers, the flexibility of cultural learning has so far received little attention in terms of cognitive mechanisms. We argue that a key feature of the flexibility of cultural learning is that both the models and learners recruit cognitive mechanisms of action coordination to modulate their behavior contingently on the behavior of their partner, generating a process of mutual adaptation supporting the successful transmission of technical skills in diverse and fluctuating learning environments. We propose that the study of cultural learning would benefit from the experimental methods, results, and insights of joint-action research and, complementarily, that the field of joint-action research could expand its scope by integrating a learning and cultural dimension. Bringing these two fields of research together promises to enrich our understanding of cultural learning, its contextual flexibility, and joint action coordination.
Individuals have a drive towards maximising action efficiency, which is reflected in action choices that minimise movement costs to reach a goal. In joint actions, actors prioritise joint efficiency ...or coefficiency, maximising the utility of the joint action even if this comes at a cost to themselves. However, it remains an open question whether actors are willing to unilaterally sacrifice their partner's individual efficiency for the greater good, when forcing a partner to incur additional costs may be interpreted as unfair. In two experiments we explored how participants would choose to distribute a motor task that required either a fair or an unfair distribution of labour. We found that, both whether there was opportunity for reciprocity (Experiment 1) or not (Experiment 2), participants maximised the coefficiency of their joint actions, regardless of how unfair this distribution of labour proved to be regarding the individual action costs. Taken together, our results suggest participants use a rational decision-making framework that prioritises overall efficiency over both individual efficiency and a consideration of fairness.
•Individual agents have a drive towards maximising the efficiency of their actions.•Individuals are also willing to sacrifice their own efficiency to maximise joint utility.•Fairness motivation could interfere with co-efficiency in task distribution.•In two experiments, we find that people reliably prioritise efficiency over fairness.•This supports action planning accounts for joint actions that emphasise rationality.
To determine whether lipid profiles and recurrent coronary heart disease (CHD) risk could be modified in patients with and without diabetes mellitus undergoing long-term cardiac rehabilitation (CR).
...Retrospective analysis of patient case records.
Community-based phase 4 CR program.
Patients without diabetes (n=154; 89% men; mean ± SD age, 59.6 ± 8.5y; body mass index BMI, 27.0 ± 3.5 kg/m²) and patients with diabetes (n=20; 81% men; mean age, 63.0 ± 8.7y; BMI, 28.7 ± 3.3 kg/m²) who completed 15 months of CR.
Exercise testing and training, risk profiling, and risk-factor education.
Cardiometabolic risk factors and 2- to 4-year Framingham recurrent CHD risk scores were assessed.
At follow up, a significant main effect for time was evident for decreased body mass and waist circumference and improved low-density lipoprotein cholesterol (LDL-C) level and submaximal cardiorespiratory fitness (all P<.05), showing the benefits of CR in both groups. However, a significant group-by-time interaction effect was evident for high-density lipoprotein cholesterol (HDL-C) level and total cholesterol (TC)/HDL-C ratio (both P<.05). TC/HDL-C ratio improved (5.0 ± 1.5 to 4.4 ± 1.3) in patients without diabetes, but showed no improvement in patients with diabetes (4.8 ± 1.6 v 4.9 ± 1.6).
We showed that numerous anthropometric, submaximal fitness, and cardiometabolic risk variables (especially LDL-C level) improved significantly after long-term CR. However, some aspects of cardiometabolic risk (measures incorporating TC and HDL-C) improved significantly in only the nondiabetic group.
The Ccr4-Not complex removes mRNA poly(A) tails to regulate eukaryotic mRNA stability and translation. RNA-binding proteins contribute to specificity by interacting with both Ccr4-Not and target ...mRNAs, but this is not fully understood. Here, we reconstitute accelerated and selective deadenylation of RNAs containing AU-rich elements (AREs) and Pumilio-response elements (PREs). We find that the fission yeast homologues of Tristetraprolin/TTP and Pumilio/Puf (Zfs1 and Puf3) interact with Ccr4-Not via multiple regions within low-complexity sequences, suggestive of a multipartite interface that extends beyond previously defined interactions. Using a two-color assay to simultaneously monitor poly(A) tail removal from different RNAs, we demonstrate that Puf3 can distinguish between RNAs of very similar sequence. Analysis of binding kinetics reveals that this is primarily due to differences in dissociation rate constants. Consequently, motif quality is a major determinant of mRNA stability for Puf3 targets in vivo and can be used for the prediction of mRNA targets.
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