Mountain View Clinical Research

Urban Air Mobility (UAM) is a rapidly emerging industry targeting delivery of air transportation services around metropolitan areas with the potential for aviation to become a practical part of everyday life while leveraging emerging technologies and operational concepts. This whitepaper, a collaboration between NASA’s Air Mobility Pathfinders (AMP) project and Wisk, explores the interoperability between crewed and uncrewed aircraft in UAM operations, leveraging operational concepts put forth in the Federal Aviation Administration (FAA) UAM Concept of Operations (ConOps) for the Midterm UAM phase. Specifically, UAM aircraft are envisioned to takeoff and land from vertiports and primarily operate within Cooperative Areas (CAs). CAs are designated as airspace volumes with specific requirements for performance, communication, and surveillance in which UAM Operators follow cooperative operating practices (COPs). Additionally, Third-Party Service Providers (TSPs) are envisioned to play a crucial role in conflict management, including both strategic and tactical deconfliction within the CAs.

As the e-commerce platforms grew exponentially, the volume of multilingual customer reviews increased, indicating that sentiment analysis is a priceless tool for finding consumer sentiment, enhancing marketing strategies, and improving customer experience. Nevertheless, emotion classification in multilingual reviews is very hard, and for one causes the variability of the language, the ambiguity of the sentiment, hierarchical word dependencies, and class imbalance, which can skew traditional models. In order to resolve such challenges, this paper introduces a T5-CapsNet ensemble model, which combines the T5 transformer for context-embedded feature extraction with Capsule Networks (CapsNet) for hierarchical sentiment learning. Furthermore, the model is further enhanced by a GAN-based data augmentation technique, which increases the number of minority class reviews in a dataset by adding synthetic minority class reviews in an effort to correct dataset imbalance and promote classification fairness. As an ensemble fusion strategy, weighted voting and stacking ensemble learning are used to improve sentiment prediction by making good use of the advantages of T5 and CapsNet. Experimental evaluations on the Multilingual Amazon Reviews Corpus (MARC) confirm that the proposed model surpasses the best sentiment classifier to reach an accuracy of 97.56%. It turns out that this hybrid deep learning approach very well captures the complex sentiment structures, or to put it differently, the multilingual e-commerce sentiment analysis largely benefited from such a hybrid deep learning approach. The findings from this study will be a foundation for building more advanced emotion classification models that can assist in improving customer sentiment analysis, automated feedback systems, as well as decision-making in global e-commerce ecosystems.

Optimal glycemic control in type 2 diabetes (T2D) remains an elusive goal despite an expanding range of effective treatments and technologies. Sustained, disciplined adherence to lifestyle modifications leads to positive outcomes in T2D, although converting medical recommendations into effective patient action is challenging in real-world practice. The emergence of AI and machine learning (ML) presents innovative opportunities to prompt effective lifestyle modifications and diabetes goal achievements in highly user-specific and user-friendly ways. The Twin Precision Treatment system is an AI-enabled bundled system of sensors and coaching that generates personalized recommendations through wearable Bluetooth-enabled technologies (including continuous glucose monitors), targeted laboratory data, Internet of Things, AI-ML algorithms, and human input. The authors designed a study to explore whether or not this bundled system intervention (INT) could help individuals — with T2D and managed in a primary care setting — achieve glycemic targets while concurrently de-escalating glucose-lowering medications. This single-center trial randomly assigned 150 adults with T2D and a body mass index (the weight in kilograms divided by the square of the height in meters) ≥27 to the INT (N=100) or a usual care (UC) control group (N=50). The primary end point was a hemoglobin A1c level (HbA1c) <6.5% (<48 mmol/mol; “target”) without glucose-lowering medications, except metformin, at 12 months; a secondary end point was the same outcome sustained for ≥90 days prior to 12 months. Other secondary end points included target attainment without any glucose-lowering medications at 12 months and ≥90 days prior to 12 months, as well as HbA1c and weight changes at 12 months. The primary end point was achieved by 71.0% (95% confidence interval [CI], 60.1 to 80.0) of INT participants versus 2.4% (95% CI, 0.5 to 11.6) of UC participants (P<0.001). Significantly more INT participants than UC participants achieved or sustained target ≥90 days prior to 12 months without glucose-lowering medications, except metformin (52.5% vs. 2.8%; P<0.001). The mean changes in HbA1c levels (−1.3% vs. −0.3%; P<0.001) and body weight (−8.6% vs. −4.6%; P<0.001) were significantly greater in the INT group than in the UC group. Other secondary end points were not significantly different. In post hoc analysis, overall use of glucose-lowering pharmacotherapy decreased markedly in the INT group but not the UC group. Quality-of-life and treatment satisfaction scores improved significantly from baseline in the INT group but not in the UC group (exploratory analyses). Overall, the AI-enabled bundled system of sensors and coaching facilitated significant improvements in glycemic control, weight loss, and quality of life versus UC, while allowing marked de-escalation of glucose-lowering pharmacotherapy.

The prelims comprise: Half-Title Page IEEE Press Editorial Board Title Page Copyright Page Table of Contents About the Author Preface Acknowledgments Acronyms

Abstract Cancer is characterized by diverse genomic alterations that change cell state and can modify therapy responses. In some cases, the relationships between genotype and drug responses are obvious, such as the response of tumors with defined oncogenic mutations to inhibitors of those mutant proteins. However, covalent KRAS G12C inhibitors (G12Ci) induce clinical responses in less than half of patients with oncogenic KRAS G12C-driven lung cancer, illustrating that genomic alterations not obviously related to the drug mechanism can have a dramatic impact. We have developed and optimized an in vivo platform to identify pharmacogenomic interactions that dictate lung cancer responses to therapies. By coupling somatic genome editing, tumor barcoding, ultra-deep barcode sequencing, and robust statistical methods in genetically engineered mouse models of human lung cancer, we can quantify and compare candidate therapies across thousands of clonal tumors of diverse tumor suppressor genotypes. This platform provides a uniquely high-throughput and quantitative assessment of tumors that are initiated de novo within the natural immunocompetent tissue environment. To uncover genotypes that are particularly important in controlling the response of tumors to oncogenic KRAS inhibition, we applied this platform to quantify the impact of 59 tumor suppressor genes on KRASG12C-driven lung tumor responses to G12Ci. Treatment resulted in approximately three-quarters reduction in both average tumor sizes and overall tumor burden, with a clear drug and dose dependence. Approximately one-quarter of inactivated genes significantly and consistently altered tumor responses across numerous G12Ci and replicate studies. Interestingly, most of these genes are thought to be in pathways not directly related to RAS signaling, and they exhibit a striking pattern in which treatment sensitivity is positively correlated with strength of the tumor suppressor effect. This pharmacogenomic profile for G12Ci was categorically different than the profiles we have observed for chemotherapy and other therapies. Overall, these results provide direct causal evidence that certain tumor suppressor genotypes dramatically shift the effectiveness of G12Ci in vivo and generate hypotheses about patients likely to benefit from G12Ci relative to alternatives. Analysis of available human data provides early clinical support for some of these hypotheses. Ongoing efforts to define pharmacogenomic profiles of combination therapies could be of even greater importance. Platforms that can accurately predict how tumor genotype drives responses have the potential to transform precision cancer therapy, enabling more effective patient stratification and therapy combinations. Citation Format: Paul J. McMurdie, Ian P. Winters, Lily M. Blair, Lafia Sebastian, Vy Tran, Gabriel Grenot, Edwin A. Apilado, Edwin A. Apilado, Ian K. Lai, Gregory D. Wall, Dmitri A. Petrov, Monte M. Winslow, Michael J. Rosen, Joseph Juan. Tumor suppressor genotype dramatically impacts lung cancer response to KRAS G12C inhibitors in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2789.

The purposes of this article are to examine the epidemiology of gestational diabetes mellitus (GDM) and subsequent type 2 diabetes, identify risk factors for the development of GDM and subsequent type 2 diabetes, discuss protocols for postpartum screening, and recommend evidence-based interventions to delay or prevent type 2 diabetes after GDM. A review of the research literature from 1995 to 2005 concerning gestational diabetes was done using MEDLINE, CINAHL, National Institutes of Health, and American Diabetes Association internet resources. The criteria set for selection included the following: the research explored risk factors for and epidemiology of gestational diabetes, the relationship of gestational diabetes and the subsequent development of type 2 diabetes, and/or the prevention of type 2 diabetes after GDM. Women with pregnancies complicated by GDM are at increased risk for subsequent development of type 2 diabetes. Research suggests that modification of lifestyle-based risk factors including obesity, poor nutrition, and lack of exercise can delay or prevent the onset of type 2 diabetes in these women. However, there is evidence that recommended postpartum screening protocols for women with GDM are not being followed; hence, those women at high risk for type 2 diabetes are not identified, and no intervention is undertaken to reduce their risks. Diabetes educators must play an integral role in increasing awareness of the need for postpartum screening and intervention for women with gestational diabetes. Only with early identification and intensive intervention can the devastating toll of diabetes be averted for many of these women.

ABSTRACT A new concept for fabricating very low profile fully-buffered dual in line memory modules (VLP FB-DIMMs) is proposed. Interconnection layers are built up on copper panels using high resolution photolithography. The copper substrate provides a built-in heat spreader. 8-high die stacks are employed. A new Copper-Pillar-Well (CPW) flip chip connector is used; it provides convenient rework of all 144 memory chips in each module, without melting any solder. The same connectors enable the Advanced Memory Buffer (AMB) chip to be attached within the width limitation of 18.3 mm for the VLP module. Improved electrical, thermal and mechanical properties are potentially achievable, enabling 16 GByte modules having advanced specifications.

Heparin-binding epidermal growth factor-like growth factor (HB-EGF), a member of the epidermal growth factor (EGF) family, is expressed during inflammatory and pathological conditions. We have cloned the rat HB-EGF and followed the expression of HB-EGF in rat kidneys treated with anti- glomerular basement membrane (anti-GBM) antibody (Ab) to induce glomerulonephritis (GN). We observed glomerular HB-EGF mRNA and protein within 30 minutes of Ab administration and showed by in situ hybridization that glomerular HB-EGF mRNA expression was predominantly in mesangial and epithelial cells. Expression of HB-EGF correlated with the onset of decreased renal function in this model. To test the direct effect of HB-EGF on renal function, we infused the renal cortex with active rHB-EGF, prepared from transfected Drosophila melanogaster cells. This treatment induced a significant decrease in single nephron GFR (SNGFR), single nephron plasma flow, and glomerular ultrafiltration coefficient and an increase in the glomerular capillary hydrostatic pressure gradient. In addition, anti-HB-EGF Ab administered just before anti-GBM Ab blocked the fall in SNGFR and GFR at 90 minutes without any change in the glomerular histologic response. These studies suggest that HB-EGF expressed early in the anti-GBM Ab GN model contributes to the observed acute glomerular hemodynamic alterations.