DNA Commission of the International Society for Forensic Genetics: Revised and extended guidelines for mitochondrial DNA typing

The DNA Commission of the International Society of Forensic Genetics (ISFG) regularly publishes guidelines and recommendations concerning the application of DNA polymorphisms to the question of human identification. Previous recommendations published in 2000 addressed the analysis and interpretation of mitochondrial DNA (mtDNA) in forensic casework. While the foundations set forth in the earlier recommendations still apply, new approaches to the quality control, alignment and nomenclature of mitochondrial sequences, as well as the establishment of mtDNA reference population databases, have been developed. Here, we describe these developments and discuss their application to both mtDNA casework and mtDNA reference population databasing applications. While the generation of mtDNA for forensic casework has always been guided by specific standards, it is now well-established that data of the same quality are required for the mtDNA reference population data used to assess the statistical weight of the evidence. As a result, we introduce guidelines regarding sequence generation, as well as quality control measures based on the known worldwide mtDNA phylogeny, that can be applied to ensure the highest quality population data possible. For both casework and reference population databasing applications, the alignment and nomenclature of haplotypes is revised here and the phylogenetic alignment proffered as acceptable standard. In addition, the interpretation of heteroplasmy in the forensic context is updated, and the utility of alignment-free database searches for unbiased probability estimates is highlighted. Finally, we discuss statistical issues and define minimal standards for mtDNA database searches.     

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

ObjectiveThe lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.Materials and MethodsWe used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.ResultsCases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.ConclusionsData source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.     

Social network influences on smoking,drinking and drug use in secondary school: centrifugal and centripetal forces

We explore how school experiences and social networks structure young people’s substance use in different institutional contexts. The concepts of ‘selection’ and ‘influence’ are situated within the context of bounded agency, counter‐school cultures and Bourdieusian notions of capital. We employed individual and group interviews, network‐mapping, and observations at two contrasting English secondary schools. Both schools were characterised by extended social network structures that appeared to influence patterns of substance use, although the mechanisms via which this occurred varied according to school context. At Grange House school (suburban context) a minority of students from disadvantaged families were alienated by the attainment‐focused regime, marginalised by a strong peer‐led centrifugal force pushing them outwards, and substance use was an alternative source of bonding and identity for these students. In contrast, at North Street a centripetal force operated whereby the majority of students were pulled towards highly‐visible, normative markers of ‘safe’, ‘road culture’, such as cannabis use and gang‐involvement, as they attempted to fit in and survive in an inner‐city school environment. We conclude that health inequalities may be reproduced through these distinctive centrifugal and centripetal forces in different institutional contexts, and this should be the focus of quantitative examination in the UK and elsewhere.     

Investigation of the effects of process variables on derived properties of spray dried solid-dispersions using polymer based response surface model and ensemble artificial neural network models

The objective of this study was to use different statistical tools to understand and optimize the spray drying process to prepare solid dispersions. In this study we investigated the relationship between input variables (inlet temperature, feed concentration, flow rate, solvent and atomization parameters) and quality attributes (yield, outlet temperature and mean particle size) of spray dried solid dispersions (SSDs) using response surface model and ensemble artificial neural network. The Box Behnken design was developed to investigate the effect of various input variables on quality attributes of final products. Moreover, Pearson correlation analysis, self organizing map, contour plots and response surface plot were used to illustrate the relationship between input variables and quality attributes. The influence of different physicochemical properties of solvent on the quality attributes of spray dried products was also investigated. Final validation of prepared models was done using binary SSDs of six model drugs with PVP. Results demonstrated the effectiveness of proposed PVP based model which can help scientists to gain detailed understanding of spray drying process of solid dispersion using minimal resources and time during early formulation development stage. It will also help them to ensure consistent quality of SSDs using broad range of input variables.     

Social Media and Nutrition Education: The Food Hero Experience

Social media can be a quick, low-cost, direct way for nutrition educators to broaden the scope of their targeted programs. The authors' viewpoint is that for social media to be effective, strategies for its use should follow “best practices” guidelines. This viewpoint suggests social media best practices based on experience gained from the Food Hero social marketing campaign. Understanding of how nutrition educators can take advantage of social media as a new mechanism for reaching their target audience is needed, including best practices for implementation, management, and evaluation.     

Cerebral energy metabolism and the brain's functional network architecture: an integrative review

Recent functional magnetic resonance imaging (fMRI) studies have emphasized the contributions of synchronized activity in distributed brain networks to cognitive processes in both health and disease. The brain''s ‘functional connectivity'' is typically estimated from correlations in the activity time series of anatomically remote areas, and postulated to reflect information flow between neuronal populations. Although the topological properties of functional brain networks have been studied extensively, considerably less is known regarding the neurophysiological and biochemical factors underlying the temporal coordination of large neuronal ensembles. In this review, we highlight the critical contributions of high-frequency electrical oscillations in the γ-band (30 to 100 Hz) to the emergence of functional brain networks. After describing the neurobiological substrates of γ-band dynamics, we specifically discuss the elevated energy requirements of high-frequency neural oscillations, which represent a mechanistic link between the functional connectivity of brain regions and their respective metabolic demands. Experimental evidence is presented for the high oxygen and glucose consumption, and strong mitochondrial performance required to support rhythmic cortical activity in the γ-band. Finally, the implications of mitochondrial impairments and deficits in glucose metabolism for cognition and behavior are discussed in the context of neuropsychiatric and neurodegenerative syndromes characterized by large-scale changes in the organization of functional brain networks.     

Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification

Background contextVariability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns.PurposeTo use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeon's treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM.Study designThis is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion.Patient sampleA total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AIS patient spine radiographies, and patients were classified according to Lenke classification.Outcome measuresFor each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded.MethodsA Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map.ResultsThe topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types.ConclusionsAn AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.     

Cortisol,salivary alpha-amylase and children’s perceptions of their social networks

In recent years there has been a growing interest in the use of social network analysis in biobehavioral research. Despite the well-established importance of social relationships in influencing human behavior and health, little is known about how children’s perception of their immediate social relationships correlates with biological parameters of stress. In this study we explore the association between two measures of children’s personal social networks, perceived network size and perceived network density, with two biomarkers of stress, cortisol and salivary alpha-amylase. Forty children (mean age = 8.30, min age = 5, and max age = 12) were interviewed to collect information about their friendships and three samples of saliva were collected. Our results show that children characterized by a lower pre-interview cortisol concentration and a lower salivary alpha-amylase reactivity to the interview reported the highest density of friendships. We discuss this result in light of the multisystem approach to the study of children’s behavioral outcomes, emphasizing that future work of this kind is needed in order to understand the cognitive and biological mechanisms underlying children’s and adolescents’ social perceptual biases.