The media dependence model: an analysis of the performance and structure of U.S. and global news

This dissertation is an attempt to make sense out of the many questions surrounding news media performance and its inadequacies. It does this by first synthesizing two critical models of news analysis and applying their respective strengths toward the other’s weaknesses. The synthesis is based on the propaganda (Herman & Chomsky, 1988, 2002, 2008) and indexing models (Bennett, 1990; Bennett, Lawrence, & Livingston, 2007). While the scope of the synthesis is broad and substantial, and contributes much in terms of understanding news content, it still leaves important questions that this dissertation endeavors to address. It answers how and why social movements garner news media attention and sympathy, while others do not. This work does not leave domestic matters unaddressed or under-theorized. It does so by distinguishing between foreign and domestic news reporting and modeling domestic coverage. It theorizes ownership of the news media in a manner appropriate for the age of globalization, with findings based on a substantial and thorough content analysis of important events in Fallujah, where the most substantial military operation was conducted during the occupation of Iraq. Lastly, in spite of containing “bird’s eye” conclusions and critical analysis on news media performance and its respective tendencies, this dissertation will also address the conditions and instances in which exceptions are most likely to arise. The name I have given to the model of news analysis presented in this dissertation is the media dependence model (MDM). I chose this name to emphasize the chief failing of the U.S. news media system: its reliance on corporate funding and ownership and the unfortunate result of this structure leading to a lack of independence from Washington (the White House and key Congressional leaders) and Wall Street (Madison Avenue and the public relations industry) positioning

Treatment Outcome-Related White Matter Differences in Veterans with Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a debilitating disorder that has been associated with brain abnormalities, including white matter alterations. However, little is known about the effect of treatment on these brain alterations. To investigate the course of white matter alterations in PTSD, we used a longitudinal design investigating treatment effects on white matter integrity using diffusion tensor imaging (DTI). Diffusion tensor and magnetization transfer images were obtained pre- and posttreatment from veterans with (n=39) and without PTSD (n=22). After treatment, 16 PTSD patients were remitted, and 23 had persistent PTSD based on PTSD diagnosis. The dorsal and hippocampal cingulum bundle, stria terminalis, and fornix were investigated as regions of interest. Exploratory whole-brain analyses were also performed. Groups were compared with repeated-measures ANOVA for fractional anisotropy (FA), and magnetization transfer ratio. Persistently symptomatic PTSD patients had increasing FA of the dorsal cingulum over time, and at reassessment these FA values were higher than both combat controls and the remitted PTSD group. Group-by-time interactions for FA were found in the hippocampal cingulum, fornix, and stria terminalis, posterior corona radiata, and superior longitudinal fasciculus. Our results indicate that higher FA of the dorsal cingulum bundle may be an acquired feature of persistent PTSD that develops over time. Furthermore, treatment might have differential effects on the hippocampal cingulum, fornix, stria terminalis, posterior corona radiata, and superior longitudinal fasciculus in remitted vs persistent PTSD patients. This study contributes to a better understanding of the neural underpinnings of PTSD treatment outcome

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease

Identification of a mechanism of photoprotective energy dissipation in higher plants

Under conditions of excess sunlight the efficient light-harvesting antenna1 found in the chloroplast membranes of plants is rapidly and reversibly switched into a photoprotected quenched state in which potentially harmful absorbed energy is dissipated as heat2, 3, a process measured as the non-photochemical quenching of chlorophyll fluorescence or qE. Although the biological significance of qE is established4, 5, 6, the molecular mechanisms involved are not7, 8, 9. LHCII, the main light-harvesting complex, has an inbuilt capability to undergo transformation into a dissipative state by conformational change10 and it was suggested that this provides a molecular basis for qE, but it is not known if such events occur in vivo or how energy is dissipated in this state. The transition into the dissipative state is associated with a twist in the configuration of the LHCII-bound carotenoid neoxanthin, identified using resonance Raman spectroscopy11. Applying this technique to study isolated chloroplasts and whole leaves, we show here that the same change in neoxanthin configuration occurs in vivo, to an extent consistent with the magnitude of energy dissipation. Femtosecond transient absorption spectroscopy12, performed on purified LHCII in the dissipative state, shows that energy is transferred from chlorophyll a to a low-lying carotenoid excited state, identified as one of the two luteins (lutein 1) in LHCII. Hence, it is experimentally demonstrated that a change in conformation of LHCII occurs in vivo, which opens a channel for energy dissipation by transfer to a bound carotenoid. We suggest that this is the principal mechanism of photoprotectio

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease