Alliance One Tobacco (Malawi)
Academic Research Repository at the Institute of Developing EconomiesNot a member yet
52305 research outputs found
Sort by
Bigger Isn’t Always Better: An Assessment of the Use of Hypersexualized Video Game Character Models and Player Preference in Video Games
No full textFrom television and movies to music videos and magazines, the media we consume has a significant impact on our attitudes, behaviors, social norms, and gender stereotypes. The sexualization of women in the media has been shown to lead to negative health outcomes such as anxiety, depression, body image issues, and eating disorders. While the impact of media such as television, movies, magazines, and social media sites have been explored previously, we have less of an understanding as to how video games as a media may affect players. This study evaluates the impact of hypersexualized character models on player preference for purchasing and playing a video game as well as the sociocultural attitudes towards appearance of the player. When given the choice between hypersexualized and normal proportioned character models, participants significantly preferred the normal character models. Furthermore, when given the choice between a male and female character model used on video game promotional art, the majority of participants preferred the female character model. No significance was found among participants’ sociocultural attitudes towards appearance. These findings illustrate the need for increased realistic female representation in video games
Viral Escape from Antibodies Targeting Conserved Sites
No full textInfluenza A virus (IAV) escape from antibody-mediated immunity causes repeated epidemics, requiring continuous monitoring and vaccine updates. The identification of monoclonal antibodies (mAbs) targeting conserved sites on influenza hemagglutinin (HA) has raised the possibility of generating vaccine-elicited antibody responses that restrict IAV escape. However, limited work has addressed how viruses may escape broadly reactive mAbs should next-generation vaccines be widely administered. To further our understanding of IAV escape, we used deep mutational scanning viral libraries, structural studies, and biochemical assays to characterize escape pathways from mAbs targeting conserved sites in HA.
The development of broadly reactive mAbs is dependent on the exposure history of an individual, intertwining virus and antibody evolution through the processes of antigenic drift and affinity maturation. We show that affinity maturation of mAbs targeting the conserved receptor binding site (RBS) restricts escape in the “imprinting” strain that originally elicited the mAb lineages. However, the barrier to escape from RBS-directed mAbs is relatively low for antigenically drifted IAVs. Amino acid positions that escaped RBS-directed mAbs are similar to those mutated in Nature.
We identified mAbs that neutralize all H1N1 or H3N2 IAVs and further engineered a pan-H1 mAb for improved neutralization potency. Although escape mutations from pan-H1 mAbs are rare in Nature, H1N1 viruses readily escaped pan-H1 mAbs in vitro. We structurally defined the HA “head” and “stem” epitopes targeted by pan-H3 mAbs, which shows unique modes of recognition compared to other mAbs. Based on these structures, we identified likely sites of escape from these pan-H3 mAbs.
We used Ramos B cell display to affinity mature an RBS-directed mAb that was isolated prior to the 2009 pandemic. We found that the addition of one or two amino acid mutations enabled recognition of a post-pandemic HA but reduced affinity to a pre-pandemic HA. However, the evolved variant maintained a high barrier to escape for the imprinting strain. These data underscore that this RBS-directed mAb is “specialized” to the strain originally recognized by the naive antibody.
This work identifies mechanisms by which IAVs escape mAbs targeting conserved sites and suggests strategies for next-generation vaccine designs.Medical SciencesMedical Science
Application of Precision Genome Editing for Neurodegenerative Disorders
No full textThe rapid development of genome editing technologies has allowed researchers to precisely and efficiently modify the mammalian genome. Remarkably, precision gene editing tools based on Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) have already entered the clinic and are being used to treat human patients. The focus on my doctoral thesis has been to apply precision gene editing tools in the development of potential treatments for neurodegenerative diseases.
First, I describe the application of base editing as a therapeutic intervention for spinal muscular atrophy (SMA). SMA is the leading genetic cause of infant mortality. SMA is caused by homozygous loss or mutation in the essential survival motor neuron 1 (SMN1) gene that leads to SMN protein insufficiency, resulting in loss of motor neurons and paralysis. SMN2 is a nearly identical gene that partially compensates for the loss of SMN1. However, SMN1 and SMN2 differ by a single C:G-to-T:A substitution at nucleotide position 6 of exon 7 (C6T) that results in exon 7 skipping in mRNA transcript and production of rapidly degraded truncated SMNΔ7 protein. We developed genome editing approaches targeting SMN2 and 1) converted SMN2 T6>C by base editing or 2) modified five SMN2 regulatory regions with nucleases or base editors to upregulate SMN levels. We determined that base editing of exon 7 C6T resulted in the greatest upregulation of SMN protein, therefore, we selected this strategy for in vivo validation. We packaged the optimized base editor into AAV9 and delivered the strategy via intracerebroventricular (ICV) injection to neonatal Δ7SMA mice, a mouse model of severe SMA. We observed 87% average T6>C conversion in the cortex and over 40% average T6>C editing in the spinal cord of the treated animals. Base editing treatment resulted in improved motor function and extended average lifespan. We extended the therapeutic window for gene editing by co-administrating with an approved SMA drug (nusinersen). This one-time co-administration resulted in further improvements in motor function and extended survival to 111 days, compared to 17 days for untreated mice. These findings demonstrate the potential of a one-time base editing treatment for SMA.
Secondly, I describe a novel application of base editors for modifying DNA repeat expansions in genes associated with trinucleotide repeat (TNR) disorders. TNR diseases are neurological movement disorders associated with somatic expansion of trinucleotide repeat sequences. TNR sequences become unstable in a length-dependent manner and are known to drive disease progression, inheritance, and anticipation. The most common pathogenic triplet sequence is ‘CAG/CTG’, which occurs in almost half of the known pathogenic TNR loci, including the HTT gene in Huntington’s Disease. The most prevalent hereditary ataxia in humans, Friedreich’s ataxia (FRDA), is caused by the expansion of ‘GAA’ repeats at the FXN locus. Small nucleotide changes within repeat tracts have been shown to reduce repeat instability in cell and animal models. In patients, benign TNR interruptions such as ‘CAA’ triplets in CAG repeats, or ‘GAG’ or ‘GGA’ in GAA repeats, are associated with reduced somatic instability. These interruptions also lead to delayed onset and progression of the disease and result in overall milder or absent clinical features compared to individuals with uninterrupted repeats of a similar length. We developed base editing strategies to introduce CAA interruptions at CAG repeats and A:T-to-G:C interruptions at GAA repeats and selected the most efficient editing approaches for in vivo validation. We administered the optimized base editors in vivo via ICV into neonatal mice and observed efficient base editing across disease-relevant tissues in mouse models of HD and FRDA. Lastly, we demonstrated that repeat interruptions significantly reduced somatic instability in the mouse brain.
Finally, I describe the first application of prime editing to remove pathogenic trinucleotide repeat expansions both in patient cells and in vivo in mice. The GAA repeat expansions found in FRDA patients are located in the intron 1 of frataxin (FXN) gene. Pathogenic GAA repeats lead to transcriptional silencing of FXN and result in frataxin protein deficiency, which is responsible for disease progression. We developed prime editing strategies that precisely remove GAA repeat expansions from FXN alleles, with a minimal loss of the surrounding regulatory or coding sequence. We delivered the optimized prime editing strategy via neonatal ICV injection to mouse models of FRDA and observed efficient excision of GAA repeats across multiple disease-relevant tissues. Furthermore, we demonstrated that PE-mediated deletion of GAA repeats results in upregulation of FXN expression in FRDA patient-derived cells and in mice
Standardization of Lipid Nanoparticle Design for Targeted Drug Delivery in Multiple Organs through a Compounded Formulaic Approach
No full textThere are many different diseases that affect a variety of different organs which makes designing drug compounds for their treatment very difficult. However, if the disease were treated using LNPs (lipid nanoparticles) and supplemental administration techniques that were developed specifically for the respective organs then we would be able to create a starting library for LNP constituents and supplemental techniques that could be utilized across a variety of organs. Lung-related diseases could utilize nebulized drug delivery 1 (NLD1) LNPs, brain-related diseases could utilize acid-degradable LNPs (AD-LNPs) with microbubble-FUS (focused ultrasound), and bone-related diseases could utilize aspartic acids LNPs (ASP6-LNPs). The LNP selection is a starting template for treatment. These diseases would incorporate relevant mRNA in their LNP encapsulation for the treatment of pneumothorax, glioblastoma, and osteogenesis imperfecta. The selection of mRNA can be switched to address different genetic anomalies seen within alternate diseases that affect the same organs. This would enable the exploration and experimentation of diseases that affect smaller disease populations as preliminary research in targeting would have already been established thus presenting a formulaic approach for the development of a therapy
Advancing the Health and Wellbeing of Addiction Treatment Providers and Hospital Workers: Working Conditions and Occupational Vicarious Trauma
No full textHealthcare workers and social service providers are the bedrock of United States’ social welfare and healthcare systems, providing critical services to millions of individuals every year. While important occupational populations unto themselves, protecting and supporting these workers is also critical to promoting the health of communities across the country. Utilizing a Total Worker Health framework and theories of stress and trauma, this dissertation focuses on contextual factors and working conditions shaping the health of two specific workforces, addiction treatment providers and low and middle-income hospital workers, with a particular focus on occupational vicarious trauma.
Paper 1 assesses the ecological factors that influenced residential addiction treatment providers’ health and wellbeing and staff turnover through qualitative analysis of interviews and focus groups of addiction treatment providers (N=49) working in residential facilities across Massachusetts. Four primary socio-contextual themes emerged from this analysis: 1) changes in substances and client needs are not reliably accompanied by shifts in treatment practices; 2) challenges with balancing state requirements and state-provided resources; 3) influence of structural discrimination and addiction stigma on pay and professional advancement for providers, many of whom are in addiction recovery themselves, and 4) geographic location of facilities shape work and quality of life in important ways. Findings from thematic analysis were used to develop a conceptual model to situate addiction treatment providers’ health and wellbeing as key components to effective addiction treatment provision, which has ramifications for improving addiction treatment for the larger population.
Paper 2 describes the development and testing of a new instrument, the Vicarious Occupational Trauma Exposure (VOTE) Index. This instrument is designed to identify where and how often workers are exposed to vicarious trauma in their work environments. Following standard measurement development methods, the VOTE Index was developed over three phases: 1) vicarious trauma exposure identification via analysis of qualitative data from Paper 1 and systematic review of literature (N=109) to develop index items; 2) index modification following cognitive interviews of addiction treatment providers (N=19) and expert feedback sessions (N=9); and 3) survey testing in a national sample of the addiction workforce (N=1,415). The VOTE Index demonstrated strong convergent validity when regressed on validated measures of psychological distress and job satisfaction; discriminant validity when regressed on participants’ interest in celebrities; and test-retest reliability as measured by intraclass correlation coefficient scores. This instrument appears to provide a valid and stable method to systematically measure vicarious occupational trauma exposure for addiction professionals.
Paper 3 investigates the prospective relationship between vicarious trauma symptoms and common gastro-intentional problems, known as disorders of gut-brain interaction, using data from the Boston Hospital Workers Health Study’s 2018 survey of hospital workers (N=775) linked to these workers’ health insurance expenditures. Multilevel logistic regression analysis indicates that participants with high vicarious trauma symptoms had 3.46-times the odds of developing disorders of gut-brain interaction compared to participants with low vicarious trauma symptoms, controlling for preexisting disorders of gut-brain interaction, demographic, work structure, and supportive work environment variables. This analysis appeared robust across several sensitivity and post-hoc analyses, indicating that vicarious trauma symptoms appear to adversely affect gastrointestinal health
Tracking and Supporting Newcomer Well-being in Science, Technology, Engineering and Mathematics
No full textBeing new to STEM can be stressful. While newcomer stress can resolve itself over time, it can also foreshadow issues like a lack of a sense of belonging, loss of interest, and ultimately intent to leave the field. While human instructors are seen as responsible for creating caring cultures in the classroom, this is not always feasible in higher education, as they must often deal with large class sizes, competing responsibilities, and a lack of indicators to signal and intervene on the student experience. Adding urgency to the matter, prior work suggests that silent struggles can be more common and detrimental to race and gender minorities in STEM. How might we scalably track and support emotional struggles in STEM classrooms? Can we support affect with targeted use of data, complementing its current use for pushing performance and accountability? My dissertation responds to these questions through a mix of experimental and design research that focuses on novice affect in STEM. Chapters one and two introduce two separate efforts to detect latent student affect for new STEM learners. Compared to study 1, which takes place in a 1:1 laboratory setting, study 2 moves detection efforts to a more ecological scenario of a group workshop, yielding lower but promising levels of performance. Study 2 additionally collects frequent stress reports through ecological momentary assessment (EMA), which is used to explore stress trends across demographic groups, as well as its impact on learning and motivation. In chapter 3, the findings from a design-based research study that tested AI-augmented periodic feedback in a makerspace course are discussed. Results show clear potential for automatic affect and motivation support, particularly through the mediating variable of classroom climate
Impacts of pathogen and host factors on the dynamics of Escherichia coli bloodstream infection
No full textBacterial bloodstream infections (BSIs) are leading causes of mortality and are poised to become increasingly difficult to treat due to rising rates of antimicrobial resistance. Developing new therapeutics is therefore of high importance and requires the use and analysis of animal models of infection. However, BSIs are highly complex infections, and we lack a thorough understanding of the host and pathogen factors that govern infection dynamics and control infection outcomes. Knowledge of several key facets of BSIs, including how microbes disseminate across the host, how different host/pathogen factors control bacterial clearance, replication, or dissemination, and how activation of innate immune factors is balanced to avoid deleterious systemic inflammation, is relatively sparse. Moreover, interactions at the host and pathogen interface yield tissue-specific phenotypes, contributing to the complexity of BSIs. A deeper understanding of the host factors and pathogen dynamics that govern BSIs in humans and animal models would inform the development of new therapeutics and provide a greater understanding of how microbes establish infection to cause disease.
This thesis describes my efforts toward expanding understanding of the host and pathogen factors that control infection dynamics during BSI. Chapter 2 describes a novel computational methodology known as STAMPR that uses barcoded bacteria to quantify bacterial replication, dissemination, and clearance within the host. In Chapter 3, we apply STAMPR to quantify the dynamics of Escherichia coli BSI following intravenous inoculation of mice. We defined several key features in this model, including identifying reservoirs of dissemination, quantifying tissue- specific patterns of clearance and bacterial replication, and characterizing host and bacterial factors that control infection dynamics.
Among the most surprising observations from these studies was the finding that E. coli replicates dramatically within liver-specific abscesses. In Chapter 4, we identify experimental, genetic, and molecular determinants of E. coli liver abscess formation. These studies suggest that hyperactivation of the innate immune response causes tissue necrosis in the liver, which serves as a site for bacterial replication, ultimately leading to abscess formation. Mice with diminished inflammatory responses, such as those lacking the lipopolysaccharide receptor TLR4, are resistant to abscess formation. Given the central role of TLR4 in sensing bacterial infection, in Chapter 5 we preform quantitative dose-response analysis using STAMPR to decipher the role of TLR4 during BSI. We describe a concept we term “dose scaling”, which relates changes in the inoculum size to the magnitude of host bottlenecks and the efficacy of the innate immune response. Our results demonstrate that during E. coli BSI, higher doses can lead to greater or reduced efficacy of innate immune responses in different tissues.
Despite the central role of TLR4 in controlling systemic inflammatory responses, TLR4 cannot solely explain variation in liver abscess susceptibility across mice. In Chapter 6, we further explore the mechanisms of liver abscess formation and find that expression of endogenous retroviruses (ERVs) correlates with abscess susceptibility. Administration of reverse transcriptase inhibitors, which may prevent accumulation of cytosolic DNA by ERV-encoded reverse transcriptases, prevents abscess formation. Reverse transcriptase inhibitors therefore represent a potential novel therapeutic for deleterious inflammatory consequences of BSIs. This dissertation concludes with a perspective on the use of barcoded bacteria to understand infection dynamics and a discussion of innate immune responses that control abscess formation.Medical SciencesMedical Science
Improved Approximation Algorithms for Bounded-Degree Local Hamiltonians
No full textWe consider the task of approximating the ground state energy of two-local quantum Hamiltonians on bounded-degree graphs. Most existing algorithms optimize the energy over the set of product states. Here we describe a family of shallow quantum circuits that can be used to improve the approximation ratio achieved by a given product state. The algorithm takes as input an n-qubit product state |v⟩ with mean energy e0=⟨v|H|v⟩ and variance Var=⟨v|(H−e0)2|v⟩, and outputs a state with an energy that is lower than e0 by an amount proportional to Var2/n. In a typical case, we have Var=Ω(n) and the energy improvement is proportional to the number of edges in the graph. When applied to an initial random product state, we recover and generalize the performance guarantees of known algorithms for bounded-occurrence classical constraint satisfaction problems. We extend our results to k-local Hamiltonians and entangled initial states.Accepted Manuscrip
Stock Market Stimulus
No full textWe study the stock market effects of the arrival of the three rounds of “stimulus checks” to U.S. taxpayers and the single round of direct payments to Hong Kong citizens. The first two rounds of U.S. checks appear to have increased retail buying and share prices of retail-dominated portfolios. The Hong Kong payments increased overall turnover and share prices on the Hong Kong Stock Exchange. We cannot rule out that these price effects were permanent. The findings raise novel questions about the role of fiscal stimulus in the stock market. (Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.)Author's Origina
Early-Stage Non-Conventional Hardware Accelerator Discovery via Optimization Methods and Compiler Analysis
No full textIn the post-Moore era, where we witness the diminishing returns of traditional transistor scaling, a pivotal transition in accelerator design methodologies has been necessitated to continually enhance power, performance, and area (PPA) characteristics. To this end, High-Level Synthesis (HLS) tools have emerged as a prominent solution, effectively translating high-level programs into specific Register-Transfer Level (RTL) implementations. These tools offer a multitude of implementation pathways, thereby accommodating the escalating demand for PPA optimization.
Concurrently, the development of advanced design space exploration (DSE) utilities has significantly refined the simulation of hardware-software co-designed systems, delivering high-fidelity point solutions without the complexity of HLS toolchains or their accompanying simulators. However, these late-stage DSE tools are not without their shortcomings; they impose a substantial manual burden on the developer to dissect the application. This task demands a nuanced understanding of computational granularity, partitioning overheads, and opportunities for reusing circuitry—insights not readily furnished by existing tools.
The prevailing trajectory in this field is to escalate the abstraction level at which designers can evaluate system configurations, thereby reducing reliance on HLS and late-stage DSE tools and enhancing automation. At the research frontier, we probe whether it is possible to derive insights into potential accelerator designs, hardware-software partitioning, and circuit reuse directly from the source code of applications coded in high-level languages like C++, supplemented by profiling data.
In this thesis, we identify the limitations and opportunities available at this level of abstraction. We discover coarse-grained patterns that help design area and energy-efficient accelerators and partitioning schemes aware of the function call-graph hierarchies. We identify sequence alignment techniques, mixed integer linear programming, and machine learning for systems as great methods to assist the next generation of system-design tools. We observe that related work does not analyze the impact of hardware models in the accelerator selection or pattern detection problems. Our analysis in these areas allows us to create tools better at selecting energy, area, and latency-efficient accelerators. Our approach allows extracting this valuable information in the earliest design stages with low error, with close-to-global optimum designs