Showing papers by "Ford Motor Company published in 2020"

The KRASG12C Inhibitor, MRTX849, Provides Insight Toward Therapeutic Susceptibility of KRAS Mutant Cancers in Mouse Models and Patients

Abstract: Despite decades of research, efforts to directly target KRAS have been challenging. MRTX849 was identified as a potent, selective, and covalent KRASG12C inhibitor that exhibits favorable drug-like properties, selectively modifies mutant cysteine 12 in GDP-bound KRASG12C and inhibits KRAS-dependent signaling. MRTX849 demonstrated pronounced tumor regression in 17 of 26 (65%) of KRASG12C-positive cell line- and patient-derived xenograft models from multiple tumor types and objective responses have been observed in KRASG12C-positive lung and colon adenocarcinoma patients. Comprehensive pharmacodynamic and pharmacogenomic profiling in sensitive and partially resistant non-clinical models identified mechanisms implicated in limiting anti-tumor activity including KRAS nucleotide cycling and pathways that induce feedback reactivation and/or bypass KRAS dependence. These factors included activation of RTKs, bypass of KRAS dependence, and genetic dysregulation of cell cycle. Combinations of MRTX849 with agents that target RTKs, mTOR, or cell cycle demonstrated enhanced response and marked tumor regression in several tumor models, including MRTX849-refractory models.

Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

Abstract: Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation. As a result, multi-agent behavior prediction has become a core component of modern human-robot interactive systems, such as self-driving cars. While there exist many methods for trajectory forecasting, most do not enforce dynamic constraints and do not account for environmental information (e.g., maps). Towards this end, we present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data (e.g., semantic maps). Trajectron++ is designed to be tightly integrated with robotic planning and control frameworks; for example, it can produce predictions that are optionally conditioned on ego-agent motion plans. We demonstrate its performance on several challenging real-world trajectory forecasting datasets, outperforming a wide array of state-of-the-art deterministic and generative methods.

Spatio-Temporal Analysis of On Demand Transit: A Case Study of Belleville, Canada

Abstract: The rapid increase in the cyber-physical nature of transportation, availability of GPS data, mobile applications, and effective communication technologies have led to the emergence of On-Demand Transit (ODT) systems. In September 2018, the City of Belleville in Canada started an on-demand public transit pilot project, where the late-night fixed-route (RT 11) was substituted with the ODT providing a real-time ride-hailing service. We present an in-depth analysis of the spatio-temporal demand and supply, level of service, and origin and destination patterns of Belleville ODT users, based on the data collected from September 2018 till May 2019. The independent and combined effects of the demographic characteristics (population density, working-age, and median income) on the ODT trip production and attraction levels were studied using GIS and the K-means machine learning clustering algorithm. The results indicate that ODT trips demand is highest for 11:00 pm-11:45 pm during the weekdays and 8:00 pm-8:30 pm during the weekends. We expect this to be the result of users returning home from work or shopping. Results showed that 39% of the trips were found to have a waiting time of smaller than 15 minutes, while 28% of trips had a waiting time of 15-30 minutes. The dissemination areas with higher population density, lower median income, or higher working-age percentages tend to have higher ODT trip attraction levels, except for the dissemination areas that have highly attractive places like commercial areas.

Olaparib and durvalumab in patients with germline BRCA-mutated metastatic breast cancer (MEDIOLA): an open-label, multicentre, phase 1/2, basket study.

Abstract: Summary Background Poly (ADP-ribose) polymerase inhibitors combined with immunotherapy have shown antitumour activity in preclinical studies. We aimed to assess the safety and activity of olaparib in combination with the PD-L1-inhibitor, durvalumab, in patients with germline BRCA1-mutated or BRCA2-mutated metastatic breast cancer. Methods The MEDIOLA trial is a multicentre, open-label, phase 1/2, basket trial of durvalumab and olaparib in solid tumours. Patients were enrolled into four initial cohorts: germline BRCA-mutated, metastatic breast cancer; germline BRCA-mutated, metastatic ovarian cancer; metastatic gastric cancer; and relapsed small-cell lung cancer. Here, we report on the cohort of patients with breast cancer. Patients who were aged 18 years or older (or aged 19 years or older in South Korea) with germline BRCA1-mutated or BRCA2-mutated or both and histologically confirmed, progressive, HER2-negative, metastatic breast cancer were enrolled from 14 health centres in the UK, the USA, Israel, France, Switzerland, and South Korea. Patients should not have received more than two previous lines of chemotherapy for metastatic breast cancer. Patients received 300 mg olaparib in tablet form orally twice daily for 4 weeks and thereafter a combination of olaparib 300 mg twice daily and durvalumab 1·5 g via intravenous infusion every 4 weeks until disease progression. Primary endpoints were safety and tolerability, and 12-week disease control rate. Safety was analysed in patients who received at least one dose of study treatment, and activity analyses were done in the full-analysis set (patients who received at least one dose of study treatment and were not excluded from the study). Recruitment has completed and the study is ongoing. This trial is registered with ClinicalTrials.gov, NCT02734004. Findings Between June 14, 2016, and May 2, 2017, 34 patients were enrolled and received both study drugs and were included in the safety analysis. 11 (32%) patients experienced grade 3 or worse adverse events, of which the most common were anaemia (four [12%]), neutropenia (three [9%]), and pancreatitis (two [6%]). Three (9%) patients discontinued due to adverse events and four (12%) patients experienced a total of six serious adverse events. There were no treatment-related deaths. 24 (80%; 90% CI 64·3–90·9) of 30 patients eligible for activity analysis had disease control at 12 weeks. Interpretation Combination of olaparib and durvalumab showed promising antitumour activity and safety similar to that previously observed in olaparib and durvalumab monotherapy studies. Further research in a randomised setting is needed to determine predictors of therapeutic benefit and whether addition of durvalumab improves long-term clinical outcomes compared with olaparib monotherapy. Funding AstraZeneca.

Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data.

Abstract: Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation. As a result, multi-agent behavior prediction has become a core component of modern human-robot interactive systems, such as self-driving cars. While there exist many methods for trajectory forecasting, most do not enforce dynamic constraints and do not account for environmental information (e.g., maps). Towards this end, we present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data (e.g., semantic maps). Trajectron++ is designed to be tightly integrated with robotic planning and control frameworks; for example, it can produce predictions that are optionally conditioned on ego-agent motion plans. We demonstrate its performance on several challenging real-world trajectory forecasting datasets, outperforming a wide array of state-of-the-art deterministic and generative methods.

Additive Manufacturing of Batteries

Abstract: Additive manufacturing, i.e., 3D printing, is being increasingly utilized to fabricate a variety of complex-shaped electronics and energy devices (e.g., batteries, supercapacitors, and solar cells) due to its excellent process flexibility, good geometry controllability, as well as cost and material waste reduction. In this review, the recent advances in 3D printing of emerging batteries are emphasized and discussed. The recent progress in fabricating 3D-printed batteries through the major 3D-printing methods, including lithographybased 3D printing, template-assisted electrodeposition-based 3D printing, inkjet printing, direct ink writing, fused deposition modeling, and aerosol jet printing, are first summarized. Then, the significant achievements made in the development and printing of battery electrodes and electrolytes are highlighted. Finally, major challenges are discussed and potential research frontiers in developing 3D-printed batteries are proposed. It is expected that with the continuous development of printing techniques and materials, 3D-printed batteries with long-term durability, favorable safety as well as high energy and power density will eventually be widely used in many fields.

Joint American Academy of Dermatology-National Psoriasis Foundation guidelines of care for the management and treatment of psoriasis in pediatric patients.

Abstract: Psoriasis is a chronic, multisystem, inflammatory disease that affects approximately 1% of children, with onset most common during adolescence. This guideline addresses important clinical questions that arise in psoriasis management and provides evidence-based recommendations. Attention will be given to pediatric patients with psoriasis, recognizing the unique physiology, pharmacokinetics, and patient-parent-provider interactions of patients younger than 18 years old. The topics reviewed here mirror those discussed in the adult guideline sections, excluding those topics that are irrelevant to, or lack sufficient information for, pediatric patients.

Effect of build orientation on the quasi-static and dynamic response of SLM AlSi10Mg

Abstract: Selective Laser Melting (SLM) allows the fabrication of complex geometries with high resolution and robust mechanical properties. However, the manner of manufacture – melting of metallic powder with a laser power source – affects microstructure and results in mechanical anisotropy. While some studies have sought to characterise the microstructure and performance of SLM AlSi10Mg, the dynamic response, particularly with regard to anisotropic effects, remains relatively undefined. To overcome this deficit AlSi10Mg specimens were fabricated using SLM with three different build orientations, and quasi-static and dynamic split-Hopkinson tensile bar tests were performed to characterise the tensile properties of the material at strain rates ranging from 3.33 x 10-2 to 2.4 x 103 s-1. The microstructure of as-manufactured specimens and fracture surfaces of failed specimens were analysed. Quasi-static and dynamic results showed little difference between build orientations with regard to strength, but components loaded perpendicular to the build direction were found to be more ductile than other build orientations. Significant scatter was observed in dynamic results, suggesting no strain rate sensitivity of the material in the tested strain rate range. Build orientation was found to affect fracture surface morphology of dynamically tested specimens due to fracture paths following melt pool boundaries. These results assist in the characterisation of the anisotropic effects of build orientation on quasi-static and dynamic behaviours of SLM AlSi10Mg towards the further commercial adoption of the manufacturing technique and material.

Racial-Ethnic Inequity in Young Adults With Type 1 Diabetes.

Abstract: CONTEXT Minority young adults (YA) currently represent the largest growing population with type 1 diabetes (T1D) and experience very poor outcomes. Modifiable drivers of disparities need to be identified, but are not well-studied. OBJECTIVE To describe racial-ethnic disparities among YA with T1D and identify drivers of glycemic disparity other than socioeconomic status (SES). DESIGN Cross-sectional multicenter collection of patient and chart-reported variables, including SES, social determinants of health, and diabetes-specific factors, with comparison between non-Hispanic White, non-Hispanic Black, and Hispanic YA and multilevel modeling to identify variables that account for glycemic disparity apart from SES. SETTING Six diabetes centers across the United States. PARTICIPANTS A total of 300 YA with T1D (18-28 years: 33% non-Hispanic White, 32% non-Hispanic Black, and 34% Hispanic). MAIN OUTCOME Racial-ethnic disparity in HbA1c levels. RESULTS Non-Hispanic Black and Hispanic YA had lower SES, higher HbA1c levels, and much lower diabetes technology use than non-Hispanic White YA (P < 0.001). Non-Hispanic Black YA differed from Hispanic, reporting higher diabetes distress and lower self-management (P < 0.001). After accounting for SES, differences in HbA1c levels disappeared between non-Hispanic White and Hispanic YA, whereas they remained for non-Hispanic Black YA (+ 2.26% [24 mmol/mol], P < 0.001). Diabetes technology use, diabetes distress, and disease self-management accounted for a significant portion of the remaining non-Hispanic Black-White glycemic disparity. CONCLUSION This study demonstrated large racial-ethnic inequity in YA with T1D, especially among non-Hispanic Black participants. Our findings reveal key opportunities for clinicians to potentially mitigate glycemic disparity in minority YA by promoting diabetes technology use, connecting with social programs, and tailoring support for disease self-management and diabetes distress to account for social contextual factors.

Cardiac substructure segmentation with deep learning for improved cardiac sparing.

Abstract: Purpose Radiation dose to cardiac substructures is related to radiation-induced heart disease. However, substructures are not considered in radiation therapy planning (RTP) due to poor visualization on CT. Therefore, we developed a novel deep learning (DL) pipeline leveraging MRI's soft tissue contrast coupled with CT for state-of-the-art cardiac substructure segmentation requiring a single, non-contrast CT input. Materials/methods Thirty-two left-sided whole-breast cancer patients underwent cardiac T2 MRI and CT-simulation. A rigid cardiac-confined MR/CT registration enabled ground truth delineations of 12 substructures (chambers, great vessels (GVs), coronary arteries (CAs), etc.). Paired MRI/CT data (25 patients) were placed into separate image channels to train a three-dimensional (3D) neural network using the entire 3D image. Deep supervision and a Dice-weighted multi-class loss function were applied. Results were assessed pre/post augmentation and post-processing (3D conditional random field (CRF)). Results for 11 test CTs (seven unique patients) were compared to ground truth and a multi-atlas method (MA) via Dice similarity coefficient (DSC), mean distance to agreement (MDA), and Wilcoxon signed-ranks tests. Three physicians evaluated clinical acceptance via consensus scoring (5-point scale). Results The model stabilized in ~19 h (200 epochs, training error 0.05) to ground truth. In four cases, DL yielded left main CA contours, whereas MA segmentation failed, and provided improved consensus scores in 44/60 comparisons to MA. DL provided clinically acceptable segmentations for all graded patients for 3/4 chambers. DL contour generation took ~14 s per patient. Conclusions These promising results suggest DL poses major efficiency and accuracy gains for cardiac substructure segmentation offering high potential for rapid implementation into RTP for improved cardiac sparing.

Tropospheric Ozone Assessment Report: A critical review of changes in the tropospheric ozone burden and budget from 1850 to 2100

Abstract: Our understanding of the processes that control the burden and budget of tropospheric ozone have changed dramatically over the last 60 years. Models are the key tools used to understand these changes and these underscore that there are many processes important in controlling the tropospheric ozone budget. In this critical review we assess our evolving understanding of these processes, both physical and chemical. We review model simulations from the IGAC Atmospheric Chemistry and Climate Model Intercomparison Project and Chemistry Climate Modelling Initiative (CCMI) to assess the changes in the tropospheric ozone burden and its budget from 1850-2010. Analysis of these data indicates that there has been significant growth in the ozone burden from 1850-2000 (~ 43±9%), but smaller growth between 1960-2000 (~16±10%) and that the models simulate burdens of ozone well within recent satellite estimates. The CCMI model ozone budgets indicate that the net chemical production of ozone in the troposphere plateaued in the 1990s and has not changed since then inspite of increases in the burden. There has been a shift in net ozone production in the troposphere being greatest in the Northern mid and high latitudes to the Northern tropics; driven by the regional evolution of precursor emissions. An analysis of the evolution of tropospheric ozone through the 21st century, as simulated by CMIP5 models, reveals a large source of uncertainty associated with models themselves (i.e. in the way that they simulate the chemical and physical processes that control tropospheric ozone). This structural uncertainty is greatest in the near term (two to three decades) but emissions scenarios dominate uncertainty in the longer-term (2050-2100) evolution of tropospheric ozone. This intrinsic model uncertainty prevents robust predictions of near-term changes in the tropospheric ozone burden, and we review how progress can be made to reduce this limitation.

Extracellular vesicles from mesenchymal stem cells reduce microglial-mediated neuroinflammation after cortical injury in aged Rhesus monkeys.

Abstract: Cortical injury, such as injuries after stroke or age-related ischemic events, triggers a cascade of degeneration accompanied by inflammatory responses that mediate neurological deficits. Therapeutics that modulate such neuroinflammatory responses in the aging brain have the potential to reduce neurological dysfunction and promote recovery. Extracellular vesicles (EVs) from mesenchymal stem cells (MSCs) are lipid-bound, nanoscale vesicles that can modulate inflammation and enhance recovery in rodent stroke models. We recently assessed the efficacy of intravenous infusions of MSC-EVs (24-h and 14-days post-injury) as a treatment in aged rhesus monkeys (Macaca mulatta) with cortical injury that induced impairment of fine motor function of the hand. Aged monkeys treated with EVs after injury recovered motor function more rapidly and more fully than aged monkeys given a vehicle control. Here, we describe EV-mediated inflammatory changes using histological assays to quantify differences in markers of neuroinflammation in brain tissue between EV and vehicle-treated aged monkeys. The activation status of microglia, the innate macrophages of the brain, is critical to cell fate after injury. Our findings demonstrate that EV treatment after injury is associated with greater densities of ramified, homeostatic microglia, along with reduced pro-inflammatory microglial markers. These findings are consistent with a phenotypic switch of inflammatory hypertrophic microglia towards anti-inflammatory, homeostatic functions, which was correlated with enhanced functional recovery. Overall, our data suggest that EVs reduce neuroinflammation and shift microglia towards restorative functions. These findings demonstrate the therapeutic potential of MSC-derived EVs for reducing neuroinflammation after cortical injury in the aged brain.

Enrollment of Older Patients, Women, and Racial/Ethnic Minority Groups in Contemporary Acute Coronary Syndrome Clinical Trials: A Systematic Review

Abstract: Importance Although age, sex, and race/ethnicity are important factors when generalizing the findings of clinical trials to routine practice, trends in the representation of these groups in contemporary acute coronary syndrome (ACS) trials are not well defined. Objective To characterize the representation of older patients, women, and racial/ ethnic minorities in ACS randomized trials. Evidence Review A systemic search was conducted of ACS trials published in 8 major medical journals between January 2001 and December 2018. Overall, 1 067 520 patients from 460 trials were included. Findings were compared with epidemiologic studies of patients with ACS. Findings The median number of participants per trial was 711 (interquartile range, 324-2163) and the median number of sites per trial was 21 (interquartile range, 5-73). Overall, 207 trials (45.0%) studied drug therapy, and 210 (45.7%) evaluated procedural interventions. The mean (SD) age of trial participants was 62.9 (10.7) years and increased from 62.3 (11.2) years in 2001-2006 to 64.0 (10.4) years in 2013-2018 (P = .01). The corresponding mean (SD) age was 66.4 (14.8) years in US epidemiologic studies and 70.0 (13.5) years in European epidemiologic studies. The overall proportion of women enrolled was 26.8% and decreased over time, from 27.8% in 2001-2006 to 24.9% in 2013-2018 (P = .21 for trend). The corresponding weighted proportions of women were 38.0% in US epidemiologic studies and 32.0% in European studies. The distribution of racial/ethnic groups was reported in only 99 trials (21.5%). In trials with reported data, 15.0% of the trial participants were nonwhite, which increased from 12.0% in 2001-2006 to 14.0% in 2013-2018. Black patients represented 3.7% of all patients during the entire study time frame, Asian patients represented 9.6%, and Hispanic patients represented 7.8%. Trends in the representation of black patients remained unchanged from 2001-2006 (5.2%) to 2013-2018 (4.9%), while the enrollment of Asian and Hispanic patients increased from 2001-2006 to 2013-2018 (from 1.9% to 10.8% for Asian patients and from 5.4% to 14.5% for Hispanic patients). Conclusions and Relevance Older patients and women are underrepresented in contemporary ACS trials compared with epidemiologic studies. Over time, there has been modest improvement in the representation of older patients but not women patients. More than three-quarters of trials did not report race/ethnicity data, with available data suggesting a modest increase in the enrollment of nonwhite patients owing to the enrollment of Asian and Hispanic patients. Enrollment of black patients remained low over time.

Driver fatigue transition prediction in highly automated driving using physiological features

Abstract: One of the main causes of traffic accidents is driver fatigue due to monotonous driving, sleep deprivation, boredom, or a combination of these. Thus, fatigue detection systems have been proposed to alert drivers. However, how early driver fatigue can be detected often determines the effectiveness of the system. Traditional approaches aim to detect driver fatigue in real time, which can be too late in many critical situations, such as the takeover transition period in highly automated driving. Therefore, in this research, we aim to predict the driver's transition from non-fatigue to fatigue in highly automated driving using physiological features. First, we capitalized on PERCLOS (i.e., PERcent of time the eyelids CLOSure) as the ground truth of driver fatigue. Next, we selected the most important physiological features to predict driver fatigue proactively. Finally, using these critical physiological features, we built prediction models that were able to predict the fatigue transition at least 13.8 s ahead of time using a technique called nonlinear autoregressive exogenous network. The accuracy of fatigue transition prediction was promising for highly automated driving (F1 measure = 97.4% and 99.1% for two types of models), which demonstrated the potential of the proposed method.

Ford Multi-AV Seasonal Dataset:

Abstract: This article presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles (AVs) at different days and times during 2017–2018. The vehicles traversed an avera...

Trial of Roflumilast Cream for Chronic Plaque Psoriasis

Abstract: Background Systemic oral phosphodiesterase type 4 (PDE-4) inhibitors have been effective in the treatment of psoriasis. Roflumilast cream contains a PDE-4 inhibitor that is being investiga...

Economic and Environmental Feasibility of Second-Life Lithium-Ion Batteries as Fast-Charging Energy Storage.

Abstract: Energy storage can reduce peak power consumption from the electricity grid and therefore the cost for fast-charging electric vehicles (EVs). It can also enable EV charging in areas where grid limitations would otherwise preclude it. To address both the need for a fast-charging infrastructure as well as management of end-of-life EV batteries, second-life battery (SLB)-based energy storage is proposed for EV fast-charging systems. The electricity grid-based fast-charging configuration was compared to lithium-ion SLB-based configurations in terms of economic cost and life cycle environmental impact in five U.S. cities. Compared to using new batteries, SLB reduced the levelized cost of electricity (LCOE) by 12-41% and the global warming potential (GWP) by 7-77%. Photovoltaics along with SLB reduced the use of grid electricity and provided higher GWP and cumulative energy demand (CED) reduction compared to only using SLB. The LCOE of the SLB-based configurations was sensitive to SLB cost, lifetime, efficiency, and discount rate, whereas the GWP and CED were affected by SLB lifetime, efficiency, and the required enclosure materials. Solar insolation and electricity pricing structures were key in determining the configuration, which was economically and environmentally suitable for a location.

Recycled poly(lactic acid)–based 3D printed sustainable biocomposites: a comparative study with injection molding

Abstract: With the objective of giving a new economic life to postindustrial waste poly(lactic acid) (PLA), biocomposites based on recycled PLA were manufactured using conventional melt blending, and the strands produced were successfully used for three-dimensional (3D) printing of American Society for Testing and Materials (ASTM) samples by means of the fused deposition molding (FDM) method. This article further discusses the processing advantages and challenges associated with void formation, anisotropic behavior, and the quality of the 3D printed (3DP) samples in comparison with injection-molded (IM) counterparts. The blends were manufactured containing 30 wt % of recycled PLA in a matrix of virgin PLA. Similarly, blends were also prepared with addition of an epoxy-based chain extender (CE) and as well as with the CE and a reinforcing phase of microcrystalline cellulose (MCC). One of the limitations of recycled PLA in FDM-based 3D printing is its high melt flow due to the reduced molecular weight after recycling, which results in excessive material flow during extrusion, and hence limits its application in FDM-based 3D printing. Consequently, the main effect of the CE, Joncryl, was to control the Melt Flow Index (MFI) of the biocomposites containing the recycled PLA. The addition of CE also resulted in improvement of the impact strength of 3DP samples. In general, the CE in combination with the natural fibers (MCC) allowed the incorporation of postindustrial PLA. Data suggest that more recycled PLA may be incorporated by using this blend combination. In general, owing to the void formation, 3DP samples presented lower values of density (lightweight) and mechanical properties as compared with IM samples. However, the tensile strength, modulus and Izod impact strength of 3DP biocomposites were increased by up to 88%, 127%, and 11%, respectively, by the addition of 5 wt % MCC, as compared with 3DP samples based on postindustrial PLA without additives.

Asymmetric Bar Winding for High-Speed Traction Electric Machines

Abstract: A new asymmetric bar winding concept along with the analysis and benefits for high-speed traction electric machines is presented. The objective is to reduce the ac losses, especially at high speeds, utilizing optimized and asymmetric conductor heights within a slot for bar-wound stators. Detailed winding diagram, height optimization, ac loss analysis, and thermal performance are presented for both symmetric, i.e., conventional, and asymmetric bar windings. The proposed idea is validated using the closed-form analytical equation and 2-D time-stepped finite-element analysis (FEA). A substantial reduction of ac losses and improvement in continuous power over the wide operation range is achieved as demonstrated for a 12-pole, 100-kW high-speed (15 000 r/min) PM traction machine. Thermal performance analysis using forced liquid cooling is also included. The design and analysis methodology is presented to support high-speed traction electric machine designers meet the ever-increasing demand on efficiency and performance with bar-type windings.

Biocarbon from peanut hulls and their green composites with biobased poly(trimethylene terephthalate) (PTT).

Abstract: There are millions of tons of post-food processing residues discarded annually. Currently, these waste materials are discarded to landfill, used as animal feed or incinerated. This suggests that there are potential uses for these materials in value-added applications. This work focuses on the characterization and valorization of peanut hulls through the generation of green composites. Peanut hulls were pyrolyzed at 500 °C and analyzed to discover their unique surface morphology and relatively low ash content. Raman spectral analysis determined ID/IG values of 0.74 for the samples, suggesting greater graphitic content than disordered carbon content. Such results were confirmed in X-ray diffraction analysis by the presence of (002) and (100) planes. Partially biobased engineering thermoplastic, poly(trimethylene terephthalate) (PTT), was combined with 20 wt.% biocarbon. The tensile and flexural moduli improved with the addition of biocarbon, and the bio-content increased from 35 to 48 wt.% as compared to neat PTT. The higher temperature biocarbon was found to have superior performance over the lower temperature sample. The enhanced sustainability of these materials suggested that peanut hulls can be valorized via thermochemical conversion to generate value-added products. Future works could focus on the optimization of these materials for non-structural automotive components or electrical housings.

AMP-Activated Protein Kinase Restricts Zika Virus Replication in Endothelial Cells by Potentiating Innate Antiviral Responses and Inhibiting Glycolysis.

Abstract: Viruses are known to perturb host cellular metabolism to enable their replication and spread. However, little is known about the interactions between Zika virus (ZIKV) infection and host metabolism. Using primary human retinal vascular endothelial cells and an established human endothelial cell line, we investigated the role of AMP-activated protein kinase (AMPK), a master regulator of energy metabolism, in response to ZIKV challenge. ZIKV infection caused a time-dependent reduction in the active phosphorylated state of AMPK and of its downstream target acetyl-CoA carboxylase. Pharmacological activation of AMPK using 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), metformin, and a specific AMPKα activator (GSK621) attenuated ZIKV replication. This activity was reversed by an AMPK inhibitor (compound C). Lentivirus-mediated knockdown of AMPK and the use of AMPKα-/- mouse embryonic fibroblasts provided further evidence that AMPK has an antiviral effect on ZIKV replication. Consistent with its antiviral effect, AMPK activation potentiated the expression of genes with antiviral properties (e.g., IFNs, OAS2, ISG15, and MX1) and inhibited inflammatory mediators (e.g., TNF-α and CCL5). Bioenergetic analysis showed that ZIKV infection evokes a glycolytic response, as evidenced by elevated extracellular acidification rate and increased expression of key glycolytic genes (GLUT1, HK2, TPI, and MCT4); activation of AMPK by AICAR treatment reduced this response. Consistent with this, 2-deoxyglucose, an inhibitor of glycolysis, augmented AMPK activity and attenuated ZIKV replication. Thus, our study demonstrates that the anti-ZIKV effect of AMPK signaling in endothelial cells is mediated by reduction of viral-induced glycolysis and enhanced innate antiviral responses.