On the origin of aurorae on Mars

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95268/1/grl20829.pd

Frontotemporal dementia: insights into the biological underpinnings of disease through gene co-expression network analysis

BACKGROUND: In frontotemporal dementia (FTD) there is a critical lack in the understanding of biological and molecular mechanisms involved in disease pathogenesis. The heterogeneous genetic features associated with FTD suggest that multiple disease-mechanisms are likely to contribute to the development of this neurodegenerative condition. We here present a systems biology approach with the scope of i) shedding light on the biological processes potentially implicated in the pathogenesis of FTD and ii) identifying novel potential risk factors for FTD. We performed a gene co-expression network analysis of microarray expression data from 101 individuals without neurodegenerative diseases to explore regional-specific co-expression patterns in the frontal and temporal cortices for 12 genes (MAPT, GRN, CHMP2B, CTSC, HLA-DRA, TMEM106B, C9orf72, VCP, UBQLN2, OPTN, TARDBP and FUS) associated with FTD and we then carried out gene set enrichment and pathway analyses, and investigated known protein-protein interactors (PPIs) of FTD-genes products. RESULTS: Gene co-expression networks revealed that several FTD-genes (such as MAPT and GRN, CTSC and HLA-DRA, TMEM106B, and C9orf72, VCP, UBQLN2 and OPTN) were clustering in modules of relevance in the frontal and temporal cortices. Functional annotation and pathway analyses of such modules indicated enrichment for: i) DNA metabolism, i.e. transcription regulation, DNA protection and chromatin remodelling (MAPT and GRN modules); ii) immune and lysosomal processes (CTSC and HLA-DRA modules), and; iii) protein meta/catabolism (C9orf72, VCP, UBQLN2 and OPTN, and TMEM106B modules). PPI analysis supported the results of the functional annotation and pathway analyses. CONCLUSIONS: This work further characterizes known FTD-genes and elaborates on their biological relevance to disease: not only do we indicate likely impacted regional-specific biological processes driven by FTD-genes containing modules, but also do we suggest novel potential risk factors among the FTD-genes interactors as targets for further mechanistic characterization in hypothesis driven cell biology work

Comparative study of the Martian suprathermal electron depletions based on Mars Global Surveyor, Mars Express and Mars Atmosphere and Volatile EvolutioN missions observations

Nightside suprathermal electron depletions have been observed at Mars by three spacecraft to date: Mars Global Surveyor, Mars Express, and the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. This spatial and temporal diversity of measurements allows us to propose here a comprehensive view of the Martian electron depletions through the first multispacecraft study of the phenomenon. We have analyzed data recorded by the three spacecraft from 1999 to 2015 in order to better understand the distribution of the electron depletions and their creation mechanisms. Three simple criteria adapted to each mission have been implemented to identify more than 134,500 electron depletions observed between 125 and 900 km altitude. The geographical distribution maps of the electron depletions detected by the three spacecraft confirm the strong link existing between electron depletions and crustal magnetic field at altitudes greater than ~170 km. At these altitudes, the distribution of electron depletions is strongly different in the two hemispheres, with a far greater chance to observe an electron depletion in the Southern Hemisphere, where the strongest crustal magnetic sources are located. However, the unique MAVEN observations reveal that below a transition region near 160–170 km altitude the distribution of electron depletions is the same in both hemispheres, with no particular dependence on crustal magnetic fields. This result supports the suggestion made by previous studies that these low-altitudes events are produced through electron absorption by atmospheric CO2

Offenders' Crime Narratives across Different Types of Crimes

The current study explores the roles offenders see themselves playing during an offence and their relationship to different crime types. One hundred and twenty incarcerated offenders indicated the narrative roles they acted out whilst committing a specific crime they remembered well. The data were subjected to Smallest Space Analysis (SSA) and four themes were identified: Hero, Professional, Revenger and Victim in line with the recent theoretical framework posited for Narrative Offence Roles (Youngs & Canter, 2012). Further analysis showed that different subsets of crimes were more like to be associated with different narrative offence roles. Hero and Professional were found to be associated with property offences (theft, burglary and shoplifting), drug offences and robbery and Revenger and Victim were found to be associated with violence, sexual offences and murder. The theoretical implications for understanding crime on the basis of offenders' narrative roles as well as practical implications are discussed

Nightside ionosphere of Mars: Modeling the effects of crustal magnetic fields and electron pitch angle distributions on electron impact ionization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95654/1/jgre2678.pd

An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks

Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn). Results: We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. Conclusions: The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful