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Rethinking Modern Agriculture: Essays On Farmers, Productivity And The Environment
No full textAgriculture industry has shown outstanding productivity growth globally in the past half a century, providing the growing population with affordable, abundant and safe food supplies but faces major challenges regarding its future production and environmental and socioeconomic impact. This dissertation illustrates that exploring “shop-floor” problems by studying farm and supply chain operations and the local context may reveal valuable solutions to major economic, social and environmental problems in agriculture. I analyze unprecedented data on micro-activities of farmers gathered through industry partnerships and conduct extensive qualitative field work to relate micro-level differences in farm practices and environment to the differences in economic, social and environmental outcomes in two major agricultural contexts: (i) Smallholder coconut and cacao farming in the Philippines, representing smallholder farming systems common in the developing world, which faces low adoption of seemingly beneficial practices, low and stagnant farm productivity and widespread farmer poverty; and (ii) large-scale corn and soybean farming in the US, representing large-scale, mechanized farming systems common in advanced economies, which faces widespread environmental degradation and low farm incomes. Major findings in the Philippines include that the best practices vary with local farm environment, micro-details of practices are strongly determinate of productivity outcomes, and effective practice adoption is associated with spatial proximity to central experts and successful adopters, which together suggest that supporting organizations should adopt a process view of practice adoption and that customized advice communicated in micro-detail, e.g. through expert visits and providing local “blueprint” farms, may enable effective dissemination of best practices and address low farm productivity and farmer poverty. Major US findings include that there are significant environmental and economic benefits to customizing practices to varying within-field environment but realizing these benefits are not economic for individual farmers without significant but possible changes in farm technology and/or environmental conservation policies. This dissertation illustrates the value of applying operations management tools and perspectives to major economic, social and environmental problems in agriculture
Statistical Methods For The Analysis And Development Of Quantitative Imaging Biomarkers
Get PDFThe field of neuroimaging statistics is concerned with elucidating meaningful conclusions from high-dimensional imaging objects, often in the form of single-dimensioned summary statistics. Ideally, these summaries should provide interpretable biomarker measurements that can guide patient diagnoses or treatment decisions while minimizing information loss associated with dimension reduction. This dissertation is focused on (1) exploring methods for analyzing previously developed imaging biomarkers and (2) developing new imaging biomarkers using both well-established and novel imaging analysis techniques. We approach this problem in three ways: in our first project, we assess how previously developed imaging biomarkers can best be incorporated into downstream analyses in the context of a clinical trial. This work conceptualizes imaging biomarkers as measurements which intrinsically contain historical information on a patient and examines the effect of incorporating these predictors on the statistical power in a clinical trial analysis. For our second project, we develop a radiomic predictor that automatically identifies an important prognostic biomarker in multiple sclerosis, relying on quantification of imaging patterns potentially associated with brain atrophy and more severe disease courses. In our third project, we construct a coordinate system and framework for multiple sclerosis lesions analyses for more sensitive and specific biomarker development. We use dimension reduction and flexible nonparametric modelling to assess the diagnostic value of this method. These methods lay the groundwork for improving future work developing and utilizing imaging biomarkers with imaging statistics
A Critical Role For Mrgprb4 Touch Neurons In A Skin-Brain Pathway For Stress Resilience
Get PDFSocial touch can act as a stress buffer, reducing behavioral and physiological responses to stressful scenarios. However, the skin-brain mechanosensory pathways that promote stress resilience remain unknown. In this thesis, I investigated how Mrgprb4-lineage touch neurons in the skin of mice play a role in this soothing skin-brain circuit. Early life ablation and activation of Mrgprb4-lineage neurons did not impact pup ultrasonic vocalizations, a measurement of postnatal stress. However, I found that mice with an early life genetic ablation of Mrgprb4-lineage touch neurons display behaviors that suggest vulnerability to stress in adulthood. Chemogenetic activation of the Mrgprb4-lineage touch neurons with whole brain activity mapping with c-Fos, uncovered distinct activity patterns in brain areas relevant to somatosensation, reward, and affect. Optogenetic activation of these neurons also promoted a conditioned place preference in female mice. Lasty, attempting to rescue deficits brought on by stress, we chemogenetically activated Mrgprb4-lineage neurons in adulthood and observed a reduction in corticosterone under mild acute stress conditions. Together, these studies reveal that Mrgprb4-lineage sensory neurons in the skin engage networks across the brain as part of a skin-brain pathway for stress resilience in mice
Machine Learning And Quantitative Neuroimaging In Epilepsy And Low Field Mri
Get PDFMedical imaging plays a key role in the diagnosis and management of neurological disorders. Magnetic resonance imaging (MRI) has proven particularly useful, as it produces high resolution images with excellent tissue contrast, permitting clinicians to identify lesions and select appropriate treatments. However, demand for MRI services has outpaced the availability of qualified experts to operate, maintain, and interpret images from these devices. Radiologists often rely on time-consuming manual analyses, which further limits throughput. Moreover, a large portion of the world’s population cannot currently access MRI, and demand for medical imaging services will continue to increase as healthcare quality improves globally. To address these challenges, we must find innovative ways to automate medical processing and produce lower-cost medical imaging devices. Recent advances in deep learning and low-field MRI hardware offer potential solutions, providing lower-cost methods for processing and collecting images, respectively. This thesis aims to develop and validate lower-cost methods for collecting and interpreting neuroimaging using machine learning algorithms and portable, low-field MRI technology. In the first section, I develop a deep learning algorithm that automatically segments resection cavities in epilepsy surgery patients and quantifies removed tissues. I also compare the impacts of epilepsy surgery on remote brain regions, demonstrating that more selective procedures minimize postoperative cortical thinning. In the second section, I explore and validate clinical applications for a new portable, low-field MRI device. Using open-source imaging and machine learning, I propose a low-cost method for simulating diagnostic performance for novel imaging devices when only sparse data is available. Additionally, I validate device performance in multiple sclerosis by directly comparing the low-field device to standard-of-care imaging using a range of manual and automated analyses. My hope is that machine learning and low-field MRI will increase medical imaging access and improve patient care worldwide
Peripheral Neuronal Encoding of Pleasurable Social Touch
No full textPleasurable touch during social behavior is key to building familial bonds and meaningful connections. As revealed by a global pandemic, isolation from these social contacts can have devastating effects on mental health. Yet, the identity and role of sensory neurons that transduce social touch remain unknown, limiting our understanding of what makes social touch beneficial and pleasurable, and therefore what goes wrong when it is missing or perturbed. A population of sensory neurons labeled by the G-protein coupled receptor Mrgprb4 detect stroking touch in mice, however, these neurons have never been implicated in any natural social behaviors. Here, we study the social relevance of Mrgprb4-lineage neurons by genetically engineering mice to allow activation or ablation of this population and reveal that these neurons are required for sexual receptivity to male mounts as well as social touch behaviors between females. Even in social isolation, optogenetic stimulation of Mrgprb4-lineage neurons through the back skin is sufficient to induce dopamine release, a conditioned place preference, and a dorsiflexion posture. This dorsiflexion resembles the natural behavioral response to social touch to the back, which is either a lordotic copulatory posture to male mounts, or a crawling posture from cagemate female contact. In the absence of Mrgprb4-lineage neurons, female mice no longer find male mounts rewarding: sexual receptivity is supplanted by aggression and a coincident decline in dopamine release. Together, these findings establish that Mrgprb4-lineage neurons are the first neurons of a skin-to-brain circuit encoding the rewarding quality of social touch. Using the same transdermal optogenetic approach, we also reveal (1) the possibility that these neurons may activate pain pathways in the context of inflammation, potentially implicating them in an allodynia phenotype, and (2) a related population of DRG neurons may provide insight into sex-differences in pain perception, yet further experimentation is necessary
Three Essays in the Sociology of Violence: Repertoire Paralysis, Localized Diffusion, and Emotional Interventions in De-escalation
No full textThe essays in this dissertation apply a micro-first sociology of violence to three topics operating at several different timescales–decades, years, days, and minutes. The first paper considers the evolution of collective violence over fifty years. This study asks whether the emergent nature of violence prevents violent forms of contention from evolving as quickly as others. Using a custom algorithm, I matched entries in a database of ethno-religious violence in India with source articles from the Times of India archive. I categorized and counted the verbs in these articles, and the same verbs in a random sample of 10,000 articles from the same time period. I find that verbs related to violence show more stability than verbs related to other forms of collective action. The second paper considers two processes of diffusion–a years-long process of violence diffusion constituting a wave of ethno-religious violence in India from 1977 to 1992, and a days-long process of diffusion where one incident of collective violence may spark others. I use an event-history framework and a recently-developed GIS data crosswalking procedure to compare demographic, electoral, economic, and diffusion variables across India at the level of parliamentary constituencies. The large- and small-scale diffusion variables are strong predictors of violence. In particular, diffusion patterns are consistent with regional language media as a vector for violence, with uptake more likely where the majority group’s economic or demographic dominance is less pronounced. The third paper considers a thirty-minute process of de-escalation after a gunman threatened to commit a school shooting. Using audio and video recordings along with a memoir, I analyze how the school bookkeeper, acting as hostage and negotiator, convinced the gunman to surrender. The turning point resulted from two emotional processes, one bolstering thebookkeeper, the other exhausting the gunman. These processes created an opening for processes of deliberation, identity repair, and rapport-building to proceed
Social Robot Augmented Telepresence for Remote Assessment and Rehabilitation of Patients with Upper Extremity Impairment
No full textWith the shortage of rehabilitation clinicians in rural areas and elsewhere, remote rehabilitation (telerehab) fills an important gap in access to rehabilitation. We have developed a first of its kind social robot augmented telepresence (SRAT) system --- Flo --- which consists of a humanoid robot mounted onto a mobile telepresence base, with the goal of improving the quality of telerehab. The humanoid has arms, a torso, and a face to play games with and guide patients under the supervision of a remote clinician. To understand the usability of this system, we conducted a survey of hundreds of rehab clinicians. We found that therapists in the United States believe Flo would improve communication, patient motivation, and patient compliance, compared to traditional telepresence for rehab. Therapists highlighted the importance of high-quality video to enable telerehab with their patients and were positive about the usefulness of features which make up the Flo system for enabling telerehab. To compare telepresence interactions with vs without the social robot, we conducted controlled studies, the first to rigorously compare SRAT to classical telepresence (CT). We found that for many SRAT is more enjoyable than and preferred over CT. The results varied by age, motor function, and cognitive function, a novel result. To understand how therapists and patients respond to and use SRAT in the wild over long-term use, we deployed Flo at an elder care facility. Therapists used Flo with their own patients however they deemed best. They developed new ways to use the system and highlighted challenges they faced. To ease the load of performing assessments via telepresence, I constructed a pipeline to predict the motor function of patients using RGBD video of them doing activities via telepresence. The pipeline extracts poses from the video, calculates kinematic features and reachable workspace, and predicts level of impairment using a random forest of decision trees. Finally, I have aggregated our findings over all these studies and provide a path forward to continue the evolution of SRAT
Sensemaking In-Between the Known and the Unknown: Narratives of Immigrant Entrepreneurs in the United States
No full textIn today\u27s hyperconnected, fast-changing environment, where uncertainty is the only certainty, individuals often find themselves in ambiguity, in-between the known and the unknown times and spaces. During ambiguous transitions, individuals deliberately engage in a cognitive process to make sense of their circumstances by generating the stories of who they are and what is happening. These stories ongoingly update a mental map that enacts a more ordered environment under liminality. This qualitative research collected and analyzed the narratives of 20 relatively resourceful immigrant entrepreneurs in the United States to explore their sensemaking during immigration, entrepreneurship, and the COVID-19 pandemic. This purposeful sampling was informed by the concept of the hybrid identity of immigrants and the emerging evidence that some resourceful immigrant entrepreneurs rely on diverse repertoires of approaches to travel across the complexity of multilayered cultural and institutional contexts. By exploring the sensemaking narratives of these immigrant entrepreneurs, the study investigated how they navigated the tensions in-between two spaces (home culture and host culture) and two times (pre-COVID-19 and during prolonged COVID-19). As a result, this study excavated detailed cognitive processes (mental dialogues weaving different elements into a holistic narrative), influencing factors (backgrounds and surroundings), and characteristics (disequilibrium, ambivalence, and randomness) of sensemaking in uncertainty and ambiguity with rich empirical accounts. Furthermore, the investigation resulted in three overarching findings. First, the study detangled multilayered contextual factors at three levels—global/national/regional, community/institutional, and family/individual—dynamically influencing immigrant entrepreneurs. Second, the study found insights into immigrant entrepreneurs\u27 identity and strategies for navigating continuous changes. Identity was constructed with both solid and fluid stories of participants\u27 values/beliefs, self-evaluations, feelings, and sense of belonging. Immigrant entrepreneurs switched on different modes of actions (develop, strive, quest, create, reflect, and retreat) and strategies (hybrid, match & connect, niche, flow, and bricolage) to respond to changing contexts. Intellectual humility facilitated the cognitive process of immigrant entrepreneurs in shifting their approaches. Third, the study highlighted the narrative mode of thinking, allowing participants to resolve identity paralysis and integrate ambivalence using metaphors and dialectical sequences
Collation Model for Ms. Codex 1237: [Notarial documents]
Get PDFA collection of 16 notarial documents dealing with property and political matters, including real estate, wills, and elections, for the city of Piacenza, Italy. The documents, originally separate, are several different sizes.Written in Piacenza, Italy, 1457-1546.https://repository.upenn.edu/sims_models/1106/thumbnail.jp
Collation Model for Ms. Codex 68: [Confessionale]
Get PDFManual for confessors and penitents with the incipit of the shorter recension (Kaeppeli) and minor variations from printed editions (reader note in pencil, flyleaf 2 recto).https://repository.upenn.edu/sims_models/1111/thumbnail.jp