Distinct forms of depression and somatization following head injury: A neuropsychological framework and exploratory treatment paradigm for somatic symptoms and executive dysfunction following mild head injury

Psychiatric symptoms following traumatic brain injury (TBI) pose a significant barrier to neurorehabilitation and impact survivor’s life satisfaction following injury. Depressive and somatization symptoms are common clinical presentations postinjury; however, due to the paucity of etiological models to explain these symptoms, treatment approaches are predominately “borrowed” from non-neurally compromised populations with similar clinical presentations. The orbitofrontal cortex (OFC) is particularly vulnerable in TBI, and serves to modulate autonomic arousal states. Varying severities of TBI have been linked to autonomic underarousal as measured by electrodermal activation (EDA). In a series of studies examining persons with mild head injury (MHI), the phenomenological presentation of depressive and somatization symptoms was examined in persons with and without MHI, and the relationship between these symptoms and autonomic underarousal was explored. In study one, MHI were found to be autonomically underaroused, reporting more somatic depressive symptoms relative to their no-MHI cohort, and the relationship between their injury severity and the intensity of their somatic depressive complaints was completely mediated by underarousal. Investigating somatization revealed MHI status as a moderator of the relationship between somatization and post-concussive symptoms, with MHI having a stronger positive association. Autonomic underarousal was found to be a complete mediator between the relationship between injury severity and their somatization symptoms. In study two, we experimentally manipulated autonomic arousal through brief cardiovascular exercise and evaluated whether this concomitantly improved somatic-based psychiatric complaints and neurocognitive functioning in persons with MHI. Study two replicated the somatic depressive mediation model of study one, and revealed that the experimental manipulation was effective in increasing autonomic arousal, and improving somatic-psychiatric complaints and neurocognitive status. Collectively, these findings suggest that depressive and somatization symptoms postinjury are phenomenologically and etiologically different in persons with a history of head injury relative to their non-neurally compromised counterpart, and autonomic underarousal and OFC dysfunction is a strong candidate for continued investigation as an etiological model for psychiatric symptoms postinjury. Reversal of underarousal may serve as an important therapeutic goal. Lastly, we propose the term “somatic underarousal” to describe this symptomatology as a means to avoid confusion with the historical roots of the term somatization

Enabling high-throughput image analysis with deep learning-based tools

Microscopes are a valuable tool in biological research, facilitating information gathering with different magnification scales, samples and markers in single-cell and whole-population studies. However, image acquisition and analysis are very time-consuming, so efficient solutions are needed for the required speed-up to allow high-throughput microscopy. Throughout the work presented in this thesis, I developed new computational methods and software packages to facilitate high-throughput microscopy. My work comprised not only the development of these methods themselves but also their integration into the workflow of the lab, starting from automating the microscopy acquisition to deploying scalable analysis services and providing user-friendly local user interfaces. The main focus of my thesis was YeastMate, a tool for automatic detection and segmentation of yeast cells and sub-type classification of their life-cycle transitions. Development of YeastMate was mainly driven by research on quality control mechanisms of the mitochondrial genome in S. cerevisiae, where yeast cells are imaged during their sexual and asexual reproduction life-cycle stages. YeastMate can automatically detect both single cells and life-cycle transitions, perform segmentation and enable pedigree analysis by determining origin and offspring cells. I developed a novel adaptation of the Mask R-CNN object detection model to integrate the classification of inter-cell connections into the usual detection and segmentation analysis pipelines. Another part of my work focused on the automation of microscopes themselves using deep learning models to detect wings of D. melanogaster. A microscope was programmed to acquire large overview images and then to acquire detailed images at higher magnification on the detected coordinates of each wing. The implementation of this workflow replaced the process of manually imaging slides, usually taking hours to do so, with a fully automated, end-to-end solution

From Dar Es Salaam To Dartmouth: A Case Study Of The Experiences Of Fogarty Aids International Training And Research Program Fellows At Dartmouth College

From Dar es Salaam to Dartmouth: A Case Study of the Experiences of Fogarty AIDS International Training and Research Program Fellows at Dartmouth Lisa Purvis, EdD Candidate, MPH, MBA Background The United States (US) is a major host nation to international college students and scholars who study a variety of disciplines (Farrugia & Bhandari, 2014). Beginning in the last decade, the demand for global health training has risen (Kanter, 2008; Kerry, Ndung\u27u, Walensky, Lees, Kayanjas, & Bangsberg, 2011). Since 2000, Geisel School of Medicine at Dartmouth has been one of 26 US universities participating in a global health training program, funded by the National Institutes of Health, Fogarty International Center\u27s AIDS International Training and Research Program (AITRP). The Fogarty AITRP annually trains scholars (Fogarty Fellows) and focuses on building HIV/AIDS research and medical capacity in low- and middle-income countries through advanced training. Along with the economic, social, and cultural assets that international students bring, are key challenges in students\u27 transition, assimilation, and acculturation. It has been observed that many of the Dartmouth Fogarty Fellows have experienced issues in matriculating at Dartmouth. Purpose of Research Little data exists on the experiences of international students studying global health in the US. Using a case study of the Fogarty Fellows at Dartmouth, this qualitative research project sought to examine their unique transition and assimilation experiences as international graduate students. A secondary purpose of the case study was to identify ways to improve students\u27 experiences. Berry\u27s theory of acculturation provided the theoretical framework for the project (Berry, 1997). Research Approach A qualitative approach was used, guided by Action Research methodology. Action Research focuses on problem-solving and it is typically defined as a reflective process of progressive problem-solving led by an individual(s), who is part of the community of practice, to address issues and solve problems (Bargal, 2008). Research Methods To gain a fuller understanding of the issues and to identify solutions to problems of acculturation that may already exist, in-depth interviews took place with 22 participants: 1) 10 alumni of the Fogarty Program at Dartmouth College; 2) 5 MPH faculty at Dartmouth; 3) 2 MPH administrators; 4) 4 Dartmouth Fogarty Program administrators; and 5) the Fogarty International Fellows Program Officer at NIH. Field research also took place in Tanzania, the home country of the Fogarty Fellows at Dartmouth, to understand the Tanzanian context. Findings Recommendations for program improvement included formalizing the program at several milestones; providing comprehensive pre-matriculation information; using technology to orient Fellows to their new environment; formal mentoring and networking; cross-cultural training with students; and orienting the faculty to the program and the needs of international students. Colleges and universities need to understand the unique experiences and the student support needs of the international student. Future research at the 25 Fogarty AITRP US-based sites is recommended before implementing any of the Dartmouth-based recommendations

Use of advanced analytics for health estimation and failure prediction in wind turbines

Tesi en modalitat de tesi per compendiThe energy sector has undergone drastic changes and critical revolutions in the last few decades. Renewable energy sources have grown significantly, now representing a sizeable share of the energy production mix. Wind energy has seen increasing rate of adoptions, being one of the more convenient and sustainable mean of producing energy. Research and innovation have helped greatly in driving down production and operation costs of wind energy, yet important challenges still remain open. This thesis addresses predictive maintenance and monitoring of wind turbines, aiming to present predictive frameworks designed with the necessities of the industry in mind. More concretely: interpretability, scalability, modularity and reliability of the predictions are the objectives —together with limited data requirements— of this project. Of all the available data at the disposal of wind turbine operators, SCADA is the principal source of information utilized in this research, due to its wide availability and low cost. Ensemble models played an important role in the development of the presented predictive frameworks thanks to their modular nature which allows to combine very diverse algorithms and data types. Important insights gained from these experiments are the beneficial effect of combining multiple and diverse sources of data —for example SCADA and alarms logs—, the easiness of combining different algorithms and indicators, and the noticeable gain in predicting performance that it can provide. Finally, given the central role that SCADA data plays in this thesis, but also in the wind energy industry, a detailed analysis of the limitations and shortcomings of SCADA data is presented. In particular, the ef- fect of data aggregation —a common practice in the wind industry— is determined developing a methodological framework that has been used to study high–frequency SCADA data. This lead to the conclusion that typical aggregation periods, i.e. 5–10 minutes that are the standard in wind energy industry are not able to capture and maintain the information content of fast–changing signals, such as wind and electrical measurements.El sector energètic ha experimentat importants canvis i revolucions en les últimes dècades. Les fonts d’energia renovables han crescut significativament, i ara representen una part important en el conjunt de generació. L’energia eòlica ha augmentat significativament, convertint-se en una de les millors alternatives per produir energia verda. La recerca i la innovació ha ajudat a reduir considerablement els costos de producció i operació de l’energia eòlica, però encara hi ha oberts reptes importants. Aquesta tesi aborda el manteniment predictiu i el seguiment d’aerogeneradors, amb l’objectiu de presentar solucions d’algoritmes de predicció dissenyats tenint en compte les necessitats de la indústria. Més concretament conceptes com, la interpretabilitat, escalabilitat, modularitat i fiabilitat de les prediccions ho són els objectius, juntament amb els requisits limitats per les de dades disponibles d’aquest projecte. De totes les dades disponibles a disposició dels operadors d’aerogeneradors, les dades del sistema SCADA són la principal font d’informació utilitzada en aquest projecte, per la seva àmplia disponibilitat i baix cost. En el present treball, els models de conjunt tenen un paper important en el desenvolupament dels marcs predictius presentats gràcies al seu caràcter modular que permet l’ús d’algoritmes i tipus de dades molt diversos. Resultats importants obtinguts d’aquests experiments són l’efecte beneficiós de combinar múltiples i diverses fonts de dades, per exemple, SCADA i dades d’alarmes, la facilitat de combinar diferents algorismes i indicadors i el notable guany en predir el rendiment que es pot oferir. Finalment, donat el paper central que SCADA l’anàlisi de dades juga en aquesta tesi, però també en la indústria de l’energia eòlica, una anàlisi detallada de la es presenten les limitacions i les mancances de les dades SCADA. En particular es va estudiar l’efecte de l’agregació de dades -una pràctica habitual en la indústria eòlica-. Dins d’aquest treball es proposa un marc metodològic que s’ha utilitzat per estudiar dades SCADA d’alta freqüència. Això va portar a la conclusió que els períodes d’agregació típics, de 5 a 10 minuts que són l’estàndard a la indústria de l’energia eòlica, no són capaços de capturar i mantenir el contingut d’informació de senyals que canvien ràpidament, com ara mesures eòliques i elèctriquesPostprint (published version

Developing well-being as one grows older : A mixed method study

Living the good life has been a topic of interest for centuries and is often captured by the phenomenon of well-being. However, there are different opinions on what wellbeing consists of and what the good life entails. The different understandings of wellbeing are typically categorized as concerned with either hedonia (feelings and experiences) or eudaimonia (positive functioning), but there are also integrated approaches that combine the two perspectives. Most would agree that the experience of both hedonia and eudaimonia can be considered the greatest form of well-being. In addition to the interest in understanding what well-being consist of, there are substantial investigations into the phenomenon’s relationship with, and effects on, other phenomena. In this thesis, old age and the capacity for self-authorship are two phenomena which well-being is investigated in relation to.publishedVersio

Cyber Law and Espionage Law as Communicating Vessels

Professor Lubin\u27s contribution is Cyber Law and Espionage Law as Communicating Vessels, pp. 203-225. Existing legal literature would have us assume that espionage operations and “below-the-threshold” cyber operations are doctrinally distinct. Whereas one is subject to the scant, amorphous, and under-developed legal framework of espionage law, the other is subject to an emerging, ever-evolving body of legal rules, known cumulatively as cyber law. This dichotomy, however, is erroneous and misleading. In practice, espionage and cyber law function as communicating vessels, and so are better conceived as two elements of a complex system, Information Warfare (IW). This paper therefore first draws attention to the similarities between the practices – the fact that the actors, technologies, and targets are interchangeable, as are the knee-jerk legal reactions of the international community. In light of the convergence between peacetime Low-Intensity Cyber Operations (LICOs) and peacetime Espionage Operations (EOs) the two should be subjected to a single regulatory framework, one which recognizes the role intelligence plays in our public world order and which adopts a contextual and consequential method of inquiry. The paper proceeds in the following order: Part 2 provides a descriptive account of the unique symbiotic relationship between espionage and cyber law, and further explains the reasons for this dynamic. Part 3 places the discussion surrounding this relationship within the broader discourse on IW, making the claim that the convergence between EOs and LICOs, as described in Part 2, could further be explained by an even larger convergence across all the various elements of the informational environment. Parts 2 and 3 then serve as the backdrop for Part 4, which details the attempt of the drafters of the Tallinn Manual 2.0 to compartmentalize espionage law and cyber law, and the deficits of their approach. The paper concludes by proposing an alternative holistic understanding of espionage law, grounded in general principles of law, which is more practically transferable to the cyber realmhttps://www.repository.law.indiana.edu/facbooks/1220/thumbnail.jp