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Safely and efficiently controlling gene expression is a long-standing goal of biomedical research, and the recently discovered bacterial CRISPR/Cas system can be harnessed to create powerful tools for epigenetic editing. Current state-of-the-art systems consist of a deactivated-Cas9 nuclease (dCas9) fused to epigenetic effector motifs/domains, along with a guide RNA (gRNA) which defines the genomic target. Such systems have been used to safely and effectively silence or activate a specific gene target under a variety of circumstances. Adeno-associated virus (AAV) vectors are the therapeutic platform of choice for delivery of genetic cargoes; nevertheless, their small packaging capacity is not suitable for the delivery of large constructs, including most CRISPR/dCas9-effector systems. To circumvent this, many AAV-based CRISPR/Cas tools are delivered separately from two viral cassettes. However, this approach requires higher viral payloads and usually is less efficient. Here we develop a compact dCas9-based repressor system packaged within a single, optimized AAV vector. The system uses a smaller dCas9 variant derived fromStaphylococcus aureus(Sa). A novel repressor was engineered by fusing the small transcription repression domain (TRD) from MeCP2 with the KRAB repression domain. The final dSaCas9-KRAB-MeCP2(TRD) construct can be efficiently packaged, along with its associated gRNA, into AAV particles. Using reporter assays, we demonstrate that the platform is capable of robustly and sustainably repressing the expression of multiple genes-of-interest, bothin vitroandin vivo. Most relevantly, we successfully silencedAPOE, the primary genetic risk factor for late onset Alzheimer’s disease (LOAD). This new platform will broaden the CRISPR/dCas9 toolset available for transcriptional manipulation of gene expression in research and therapeutic settings.

With increases in unemployment and individuals experiencing mental health symptoms, the effects of the COVID-19 pandemic have become a prevalent issue in society. The purpose of this paper is to educate communities about the current effects of COVID-19 on the economy as well as mental health. We aim to establish connections between economic hardships and patterns of depression and anxiety across different demographic groups within the U.S. labor force participation pool aged 18 or older. We analyzed existing literature on past recessions, past pandemics, and the current pandemic’s impacts. The findings from past literature suggest that the pandemic has caused various economic impacts which in turn affect mental health, particularlyanxiety and depression rates. We used data from the U.S. Census Bureau Household Pulse Survey (HPS) to conduct chi-squared tests of independence on employment income loss and symptoms of anxiety and depression over 12 weeks of the pandemic. For each demographic variable (race, income level, gender, and age), we found an association between each categoryand experienced employment income loss. The tests yielded the same conclusions for the data of the symptoms of anxiety and depression.

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

With increases in unemployment and individuals experiencing mental health symptoms, the effects of the COVID-19 pandemic have become a prevalent issue in society. The purpose of this paper is to educate communities about the current effects of COVID-19 on the economy as well as mental health. We aim to establish connections between economic hardships and patterns of depression and anxiety across different demographic groups within the U.S. labor force participation pool aged 18 or older. We analyzed existing literature on past recessions, past pandemics, and the current pandemic’s impacts. The findings from past literature suggest that the pandemic has caused various economic impacts which in turn affect mental health, particularly anxiety and depression rates. We used data from the U.S. Census Bureau Household Pulse Survey (HPS) to conduct chi-squared tests of independence on employment income loss and symptoms of anxiety and depression over 12 weeks of the pandemic. For each demographic variable (race, income level, gender, and age), we found an association between each category and experienced employment income loss. The tests yielded the same conclusions for the data of the symptoms of anxiety and depression.

The paradigm of aging-in-place — where the elderly live and age in their own homes, independently and safely, with care provided by the community — is compelling, especially in societies that face both shortages in institutionalized eldercare resources, and rapidly-aging populations. Internet-of-Things (IoT) technologies, particularly in-home monitoring solutions, are commercially available, and can be a fundamental enabler of smart community eldercare, if they are dependable. In this paper, we present our findings on system performance of solutions from two vendors, which we have deployed at scale for technology-enabled community care. In particular, we highlight the importance of quantifying actual system performance, which may differ from perceived performance.

Trophic state of lakes has been related to catchment land use, but direct links between phytoplankton taxa and land use are limited. Phytoplankton composition, represented by relative cell abundance of phyla, was measured over a period of 4 years in 11 lakes in the Rotorua region, New Zealand. The lakes differed in morphometry, trophic state and land use (as percentage catchment area). We tested whether relative proportion of land uses, indirectly representing relative nutrient loading, was the overarching driver of phytoplankton composition. Trophic state was correlated negatively with native forest and positively with pasture and urban area. Cyanoprokaryota were correlated negatively with native forest and positively with pasture and trophic state, Chlorophyta were correlated positively with native forest and urban land use and negatively with pasture and trophic state, and Bacillariophyta were positively correlated with dissolved reactive silica to dissolved inorganic nitrogen (Si:DIN) and Si to dissolved reactive phosphorus (Si:DRP) ratios. Lakes with higher nutrient loads had higher trophic state and Cyanoprokaryota dominance. Chlorophyta were negatively correlated with Cyanoprokaryota and Bacillariophyta, suggesting competition amongst these groups. Our results apply to lakes potentially subject to changes in catchment land use, which may have implications for trophic state, phytoplankton composition and Cyanoprokaryota blooms.