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Our classical strain improvement began with an isolate showing 28% palmitic and 60% oleic acids. UV and chemical mutagenesis enhanced our strain’s productivity, carbon yield, and oleic acid content. The iterative methodology we used involved the creation of mutant libraries followed by clonal isolation, assessments of feedstock utilization and growth, oil titer, and the validation of oil composition. Screening these libraries facilitated the identification of isolates with the ability to produce elevated levels of oleic acid, aligning with the targets for high-oleic acid substitutes. Utilizing a classical strain improvement approach, we successfully isolated a high-oleic acid strain wherein the level of oleic acid was increased from 60 to >86% of total FA. The performance of the classically improved high oleic acid-producing strain was assessed at fermentation scales ranging from 1 L to 4000 L, demonstrating the utility of our strain and process at an industrial scale. These oils offer promise in various applications across both the food and industrial sectors, with the added potential of furthering sustainability and health-conscious initiatives.

OBJECTIVES:. Transcranial Doppler (TCD) has been evaluated as a noninvasive intracranial pressure (ICP) assessment tool. Correction for insonation angle, a potential source of error, with transcranial color-coded sonography (TCCS) has not previously been reported while evaluating ICP with TCD. Our objective was to study the accuracy of TCCS for detection of ICP elevation, with and without the use of angle correction. DESIGN:. Prospective study of diagnostic accuracy. SETTING:. Academic neurocritical care unit. PATIENTS:. Consecutive adults with invasive ICP monitors. INTERVENTIONS:. Ultrasound assessment with TCCS. MEASUREMENTS AND MAIN RESULTS:. End-diastolic velocity (EDV), time-averaged peak velocity (TAPV), and pulsatility index (PI) were measured in the bilateral middle cerebral arteries with and without angle correction. Concomitant mean arterial pressure (MAP) and ICP were recorded. Estimated cerebral perfusion pressure (CPP) was calculated as estimated CPP (CPPe) = MAP × (EDV/TAPV) + 14, and estimated ICP (ICPe) = MAP–CPPe. Sixty patients were enrolled and 55 underwent TCCS. Receiver operating characteristic curve analysis of ICPe for detection of invasive ICP greater than 22 mm Hg revealed area under the curve (AUC) 0.51 (0.37–0.64) without angle correction and 0.73 (0.58–0.84) with angle correction. The optimal threshold without angle correction was ICPe greater than 18 mm Hg with sensitivity 71% (29–96%) and specificity 28% (16–43%). With angle correction, the optimal threshold was ICPe greater than 21 mm Hg with sensitivity 100% (54–100%) and specificity 30% (17–46%). The AUC for PI was 0.61 (0.47–0.74) without angle correction and 0.70 (0.55–0.92) with angle correction. CONCLUSIONS:. Angle correction improved the accuracy of TCCS for detection of elevated ICP. Sensitivity was high, as appropriate for a screening tool, but specificity remained low.

Abstract Background Traumatic Brain Injury (TBI) is a common condition with potentially severe long-term complications, the prediction of which remains challenging. Machine learning (ML) methods have been used previously to help physicians predict long-term outcomes of TBI so that appropriate treatment plans can be adopted. However, many ML techniques are “black box”: it is difficult for humans to understand the decisions made by the model, with post-hoc explanations only identifying isolated relevant factors rather than combinations of factors. Moreover, such models often rely on many variables, some of which might not be available at the time of hospitalization. Methods In this study, we apply an interpretable neural network model based on tropical geometry to predict unfavorable outcomes at six months from hospitalization in TBI patients, based on information available at the time of admission. Results The proposed method is compared to established machine learning methods—XGBoost, Random Forest, and SVM—achieving comparable performance in terms of area under the receiver operating characteristic curve (AUC)—0.799 for the proposed method vs. 0.810 for the best black box model. Moreover, the proposed method allows for the extraction of simple, human-understandable rules that explain the model’s predictions and can be used as general guidelines by clinicians to inform treatment decisions. Conclusions The classification results for the proposed model are comparable with those of traditional ML methods. However, our model is interpretable, and it allows the extraction of intelligible rules. These rules can be used to determine relevant factors in assessing TBI outcomes and can be used in situations when not all necessary factors are known to inform the full model’s decision.

Abstract The Faraday forcing method in levitated liquid droplets has recently been introduced as a method for measuring surface tension using resonance. By subjecting an electrostatically levitated liquid metal droplet to a continuous, oscillatory, electric field, at a frequency nearing that of the droplet’s first principal mode of oscillation (known as mode 2), the method was previously shown to determine surface tension of materials that would be particularly difficult to process by other means, e.g., liquid metals and alloys. It also offers distinct advantages in future work involving high viscosity samples because of the continuous forcing approach. This work presents (1) a benchmarking experimental method to measure surface tension by excitation of the second principal mode of oscillation (known as mode 3) in a levitated liquid droplet and (2) a more rigorous quantification of droplet excitation using a projection method. Surface tension measurements compare favorably to literature values for Zirconium, Inconel 625, and Rhodium, using both modes 2 and 3. Thus, this new method serves as a credible, self-consistent benchmarking technique for the measurement of surface tension.

Background: Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI’s Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). Materials and methods: As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. Results: Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. Conclusion: To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.

Introduction Less than 40% of patients with ovarian cancer (OC) in the USA receive stage-appropriate guideline-adherent surgery and chemotherapy. Black patients with cancer report greater depression, pain and fatigue than white patients. Lack of access to healthcare likely contributes to low treatment rates and racial differences in outcomes. The Ovarian Cancer Epidemiology, Healthcare Access and Disparities study aims to characterise healthcare access (HCA) across five specific dimensions—Availability, Affordability, Accessibility, Accommodation and Acceptability—among black, Hispanic and white patients with OC, evaluate the impact of HCA on quality of treatment, supportive care and survival, and explore biological mechanisms that may contribute to OC disparities.Methods and analysis We will use the Surveillance Epidemiology and Ends Results dataset linked with Medicare claims data from 9744 patients with OC ages 65 years and older. We will recruit 1641 patients with OC (413 black, 299 Hispanic and 929 white) from cancer registries in nine US states. We will examine HCA dimensions in relation to three main outcomes: (1) receipt of quality, guideline adherent initial treatment and supportive care, (2) quality of life based on patient-reported outcomes and (3) survival. We will obtain saliva and vaginal microbiome samples to examine prognostic biomarkers. We will use hierarchical regression models to estimate the impact of HCA dimensions across patient, neighbourhood, provider and hospital levels, with random effects to account for clustering. Multilevel structural equation models will estimate the total, direct and indirect effects of race on treatment mediated through HCA dimensions.Ethics and dissemination Result dissemination will occur through presentations at national meetings and in collaboration with collaborators, community partners and colleagues across othercancer centres. We will disclose findings to key stakeholders, including scientists, providers and community members. This study has been approved by the Duke Institutional Review Board (Pro00101872). Safety considerations include protection of patient privacy. All disseminated data will be deidentified and summarised.

The need for clean energy production is increasing due to our society’s high energy consumption, decreasing fossil fuel reserves, and growing environmental issues. Implementation of renewable energy systems on university campuses would best demonstrate academic institutions’ efforts to promote sustainability. In this research study, multiple locations were assessed to determine the feasibility of installing parking canopies coupled with solar photovoltaic (PV) systems and electric vehicle (EV) charging stations and to show these two systems’ effectiveness if they are implemented together. We reviewed the trend and growing demand towards electric vehicles and examined different solar panels and mounts to find the most optimal combination for the suggested study sites. Through the data collection on the study sites and the utilization of simulation tools we were able to obtain estimations of these two systems at different sites, the energy production of the systems, and carbon emissions reduction potential. As a result of the proposed study, three viable locations were selected to offer PV-EV parking canopies. A cost-benefit analysis was performed at each location to provide useful energy production and financial data to determine the optimal location. The results provided insightful implications to decision makers at the university. The implementation of solar PV and EV charging stations combined gives the university the ability to implement clean, renewable energy in order to show its commitment to sustainability as well as supporting the ability of students, commuters and faculty to switch from gas powered vehicles to electric vehicles.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) frequently causes hospital-acquired infections and is associated with high morbidity and mortality. CRKP can have multiple resistance mechanisms and only a few can be routinely detected by commercial molecular or phenotypic assays making surveillance for CRKP particularly challenging. In this report, we identified and characterized an unusual non–carbapenemase-producing CRKP carrying a rare plasmid-borne inducible AmpC gene, blaDHA-1. The isolate was recovered from blood culture of a 67-year-old female presenting with sepsis post bladder surgery and ureteral stent removal. The primary isolate displayed an indeterminate susceptibility pattern for ceftriaxone by broth microdilution, but was susceptible by disk diffusion with one colony growing within the zone of inhibition. The ceftriaxone resistant colony was sub-cultured and had a minimum inhibitory concentration (MIC) of 2 ug/ml for imipenem (intermediate) and a zone size of 18 mm for ertapenem (resistant), but remained susceptible to cefepime and meropenem. Further phenotypic characterization of this sub-cultured isolate showed carbapenemase activity. Whole genome sequencing (WGS) revealed the presence of two subpopulations of a K. pneumoniae (MLST sequence type 11) from the primary blood culture isolate: one pan-susceptible to beta-lactams tested and the other resistant to the 3rd generation cephalosporins and ertapenem. WGS analysis identified the resistant K. pneumoniae harboring IncFIB(K) and IncR plasmids and the presence of plasmid-borne beta-lactam resistance genes blaOXA-1 and blaDHA-1, an inducible AmpC gene. Additional resistance genes against quinolones (aac(6′)-Ib-cr, oqxA, oqB), aminoglycoside (aph(3′)-Ia), sulfonamide (sul1), and tetracycline (tet(A)) were also identified. DHA-1 positive K. pneumoniae have been previously identified outside the US, particularly in Asia and Europe, but limited cases have been reported in the United States and may be underrecognized. Our study highlights the importance of using both extended phenotypic testing and WGS to identify emerging resistance mechanisms in clinical Enterobacterales isolates with unusual antimicrobial resistance patterns.

Aeromonas hydrophila resides in a variety of aquatic environments. Infections with A. hydrophila mainly occur after contact with fresh or brackish water. Nosocomial infections with A. hydrophila can also occur. A. hydrophila infections can be difficult to treat due to both intrinsic and acquired antimicrobial resistance (AMR) mechanisms. In 2018–19, we isolated multi-drug resistant (MDR) A. hyrodphila from two solid organ transplant patients with intra-abdominal infections. We aimed to characterize their AMR mechanisms and to determine their genetic relatedness to aid epidemiological investigation. We performed whole genome sequencing (WGS) using Illumina MiSeq and Nanopore MinIon on 3 A. hydrophila isolates, with one isolate from Patient A (blood) and two isolates from Patient B (abdominal and T-tube fluid, isolated 2 weeks apart). Phenotypic assays included: Broth Microdilution (BMD), Modified Hodge Test (MHT), Modified Carbapenem Inactivation Method (mCIM), and EDTA Carbapenem Inactivation Method (eCIM). Data analyses were performed using CLCbio and Geneious. AMR genomic analysis revealed that all three isolates possess chromosomally encoded genes including blaOXA−12(oxacillinase), blacepS(AmpC), and blacphA7(metallo-beta-lactamase). All isolates tested strongly positive by MHT and mCIM, but only Patient B's second isolate (after 2 weeks of meropenem treatment) tested positive by eCIM. More intriguingly, Patient B's first isolate (before meropenem treatment) tested falsely susceptible to carbapenems by BMD, suggesting blacphA7 gene was not expressed constitutively. Phylogenetic analysis showed the two isolates from Patient B were highly similar with only 1 SNP difference. The isolate from Patient A only differed from Patient B's isolates by 35 and 36 SNPs, respectively, suggesting close genetic relatedness. Further epidemiological investigation is undergoing. We report the first cases of CphA-mediated carbapenem resistant A. hydrophila in the U.S. It is concerning that 1 out of 3 isolates tested falsely susceptible to carbapenems by BMD despite clear carbapenemase production shown by strongly positive MHT and mCIM. In both cases, meropenem was initially used to treat the patients. Clinicians and microbiologists in the US should be aware of the emerging MDR Aeromonas nosocomial infections and the potential false carbapenem susceptible results due to CphA-type carbapenemase, which may be induced during treatment.

Business Improvement Districts (BIDs) are a contemporary urban revitalization policy that has been set in motion through international policymaking circuits. They have been presented as a panacea to the economic and social challenges facing many cities and traditional shopping districts. However, a comprehensive overview of the academic literature on this form of local governance remains to be conducted. Drawing on bibliometric methods and bibliometrix R-tool, this paper maps and examines the state-of-the-art of academic knowledge on BIDs published between 1979 and 2021. Findings suggest that (i) scientific production has increased since the early 2000s, has crossed US borders but remains highly Anglo-Saxon-centered; (ii) academic knowledge on BIDs is multidisciplinary and has been published in high-impact journals; (iii) influential documents on BIDs have centered on three issues: urban governance/politics, policy mobilities–mutation and impacts assessment and criticisms; (iv) while author collaboration networks exist, the interaction between them is limited; (v) the conceptualization of BIDs has changed over time, both in thematic and geographical focus. These results constitute the first science mapping on the academic literature on BIDs, and we argue they should inform future scientific debates about the studying of this form of local governance.

Objective Non-AIDS-defining cancers (NADCs) have emerged as significant contributors to cancer mortality and morbidity among persons living with HIV (PLWH). Because NADCs are also associated with many social and behavioural risk factors that underlie HIV, determining the extent to which each of these factors contributes to NADC risk is difficult. We examined cancer incidence and mortality among persons with a history of incarceration, because distributions of other cancer risk factors are likely similar between prisoners living with HIV and non-infected prisoners.Design Registry-based retrospective cohort study.Participants Cohort of 22 422 persons incarcerated in Georgia, USA, prisons on 30 June 1991, and still alive in 1998.Outcome measures Cancer incidence and mortality were assessed between 1998 and 2009, using cancer and death registry data matched to prison administrative records. Age, race and sex-adjusted standardised mortality and incidence ratios, relative to the general population, were calculated for AIDS-defining cancers, viral-associated NADCs and non-infection-associated NADCs, stratified by HIV status.Results There were no significant differences in cancer mortality relative to the general population in the cohort, regardless of HIV status. In contrast, cancer incidence was elevated among the PLWH. Furthermore, incidence of viral-associated NADCs was significantly higher among PLWH versus those without HIV infection (standardised incidence ratio=6.1, 95% CI 3.0 to 11.7, p