The bio-recognition capabilities of materials-specific peptides offer a promising route to obtaining and organizing 2D nanosheet materials in aqueous media. Although significant advances have been made for graphene, little is currently understood regarding how to apply this strategy to hexagonal boron nitride (h-BN) due to a lack of knowledge regarding peptide/h-BN interactions. Here, one of the few peptide sequences known with affinity for h-BN, BP7, is the focus of mutation studies and bio-conjugation. A combination of experimental methods and modeling reveals the importance of Tyrosine in peptide/h-BN interactions. This residue is identified as the key anchoring species, which is then leveraged via bio-conjugation of BP7 to a fatty acid to create new interfacial properties. Specific placement of the fatty acid in the bio-conjugate results in dramatic manipulation of the surface-bound biotic overlayer to generate a highly viscoelastic interface. This viscoelasticity is a consequence of the fatty acid binding, which also down-modulates Tyrosine contact to h-BN, resulting in presentation of the extended peptide to solution. In this orientation, the biomolecule is available for subsequent bioconjugation, providing new pathways to programmable organization and conjugation of h-BN nanosheets in liquid water.

Vulvar squamous cell carcinoma (VSCC) comprises two distinct etiopathological subtypes: i) Human papilloma virus (HPV)-related VSCC, which arises via the precursor high grade squamous intraepithelial lesion (HSIL); and ii) HPV-independent VSCC, which arises via precursor, differentiated vulvar intraepithelial neoplasia (dVIN), driven by TP53 mutations. However, the mechanism of carcinogenesis of VSCC is poorly understood. The current study aimed to gain insight into VSCC carcinogenesis by identifying differentially expressed genes (DEGs) for each VSCC subtype. The expression of certain DEGs was then further assessed by performing immunohistochemistry (IHC) on whole tissue sections of VSCC and its precursors. Statistical analysis of microarrays was performed on two independent gene expression datasets (GSE38228 and a study from Erasmus MC) on VSCC and normal vulva. DEGs were identified that were similarly (up/down) regulated with statistical significance in both datasets. For HPV-related VSCCs, this constituted 88 DEGs, and for HPV-independent VSCCs, this comprised 46 DEGs. IHC was performed on VSCC (n=11), dVIN (n=6), HSIL (n=6) and normal vulvar tissue (n=7) with i) signal transducer and activator of transcription 1 (STAT1; an upregulated DEGs); ii) nuclear factor IB (NFIB; a downregulated DEG); iii) p16 (to determine the HPV status of tissues); and iv) p53 (to confirm the histological diagnoses). Strong and diffuse NFIB expression was observed in the basal and para-basal layers of normal vulvar tissue, whereas NFIB expression was minimal or completely negative in dVIN and in both subtypes of VSCC. In contrast, no discernable difference was observed in STAT1 expression among normal vulvar tissue, dVIN, HSIL or VSCC. By leveraging bioinformatics, the current study identified DEGs that can facilitate research into VSCC carcinogenesis. The results suggested that NFIB is downregulated in VSCC and its relevance as a diagnostic/prognostic biomarker deserves further exploration.

Road transportation is one of the largest sectors of greenhouse gas (GHG) emissions affecting climate change. Tackling climate change as a global community will require new capabilities to measure and inventory road transport emissions. However, the large scale and distributed nature of vehicle emissions make this sector especially challenging for existing inventory methods. In this work, we develop machine learning models that use satellite imagery to perform indirect top-down estimation of road transport emissions. Our initial experiments focus on the United States, where a bottom-up inventory was available for training our models. We achieved a mean absolute error (MAE) of 39.5 kg CO2 of annual road transport emissions, calculated on a pixel-by-pixel (100 m2) basis in Sentinel-2 imagery. We also discuss key model assumptions and challenges that need to be addressed to develop models capable of generalizing to global geography. We believe this work is the first published approach for automated indirect top-down estimation of road transport sector emissions using visual imagery and represents a critical step towards scalable, global, near-real-time road transportation emissions inventories that are measured both independently and objectively.

Although the inclusion of individuals with lived experience is encouraged within the research process, there remains inconsistent direct involvement in many mental health fields. Within the eating disorders field specifically, there is a very strong and increasing presence of lived experience advocacy. However, due to a number of potential challenges, research undertaken in consultation or in collaboration with individuals with lived experience of an eating disorder is scarce. This paper describes the significant benefits of the inclusion of individuals with lived experience in research. The specific challenges and barriers faced in eating disorders research are also outlined. It is concluded that in addition to existing guidelines on working with lived experience collaborators in mental health research, more specific procedures are required when working with those with eating disorders.

Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large. It is, therefore, almost impossible to implement the DL method in memory-constrained Internet-of-Things (IoT) devices. In this article, we reduce the feature dimensionality of large-scale IoT network traffic data using the encoding phase of long short-term memory autoencoder (LAE). In order to classify network traffic samples correctly, we analyze the long-term inter-related changes in the low-dimensional feature set produced by LAE using deep bidirectional long short-term memory (BLSTM). Extensive experiments are performed with the BoT-IoT data set to validate the effectiveness of the proposed hybrid DL method. Results show that LAE significantly reduced the memory space required for large-scale network traffic data storage by 91.89%, and it outperformed state-of-the-art feature dimensionality reduction methods by 18.92–27.03%. Despite the significant reduction in feature size, the deep BLSTM model demonstrates robustness against model underfitting and overfitting. It also achieves good generalisation ability in binary and multiclass classification scenarios.

Acetone is a metabolic byproduct found in the exhaled breath and can be measured to monitor the metabolic degree of ketosis. In this state, the body uses free fatty acids as its main source of fuel because there is limited access to glucose. Monitoring ketosis is important for type I diabetes patients to prevent ketoacidosis, a potentially fatal condition, and individuals adjusting to a low-carbohydrate diet. Here, we demonstrate that a chemiresistor fabricated from oxidized single-walled carbon nanotubes functionalized with titanium dioxide (SWCNT@TiO2) can be used to detect acetone in dried breath samples. Initially, due to the high cross sensitivity of the acetone sensor to water vapor, the acetone sensor was unable to detect acetone in humid gas samples. To resolve this cross-sensitivity issue, a dehumidifier was designed and fabricated to dehydrate the breath samples. Sensor response to the acetone in dried breath samples from three volunteers was shown to be linearly correlated with the two other ketone bodies, acetoacetic acid in urine and β-hydroxybutyric acid in the blood. The breath sampling and analysis methodology had a calculated acetone detection limit of 1.6 ppm and capable of detecting up to at least 100 ppm of acetone, which is the dynamic range of breath acetone for someone with ketosis. Finally, the application of the sensor as a breath acetone detector was studied by incorporating the sensor into a handheld prototype breathalyzer.

There is a pressing need to characterise the nature, extent and duration of immune response to SARS-CoV-2 in cancer patients, to inform risk-reduction strategies and preserve cancer outcomes. CAPTURE is a prospective, longitudinal cohort study of cancer patients and healthcare workers (HCWs) integrating immune profiles and clinical annotation. We evaluated 529 blood samples and 1051 oronasopharyngeal swabs from 144 cancer patients and 73 HCWs and correlated with >200 clinical variables. In patients with solid cancers and HCWs, S1-reactive and neutralising antibodies to SARS-CoV-2 were detectable five months post-infection. In these participants, SARS-CoV-2-specific T-cell responses were detected. CD4+ T-cell response correlated with S1 antibody levels. Patients with haematological malignancies had impaired but partially compensated immune responses, depending on malignancy and therapy. Overall, cancer stage, disease status, and therapies did not correlate with immune responses. These findings have implications for understanding individual risks and potential effectiveness of SARS-CoV-2 vaccination in this population. Citation Format: Lewis Au, Annika Fendler, Laura Amanda Boos, Fiona Byrnes, Scott Shepherd, Emma Nicholson, Scaheen Kumar, Nadia Yousaf, Katalin Wilkinson, Anthony Swerdlow, Ruth Harvey, George Kassiotis, Robert Wilkinson, James Larkin, Samra Turajlic. Adaptive immunity to SARS-CoV-2 in cancer patients: The CAPTURE study [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr S03-02.

Background: Declining humoral immunity in COVID-19 patients and possibility of reinfections has raised concern. Mucosal immunity particularly salivary antibodies could be short-lived. However, long-term studies are sparse. Methods: Using a multiplex bead-based array platform, we investigated antibodies specific to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins in 256 saliva samples from convalescent patients 1-9 months after symptomatic COVID-19 (n=74, Cohort 1), undiagnosed individuals with self-reported questionnaires (n=147, Cohort 2), and individuals sampled pre-pandemic time (n= 35, Cohort 3). Results: Salivary IgG antibody responses in Cohort 1 (mainly mild COVID-19) were detectable up to 9 month recovery, with high correlations between spike and nucleocapsid specificity. At 9 months, IgG remained in saliva in majority as seen in blood serology. Salivary IgA was rarely detected at this timepoint. In Cohort 2, salivary IgG and IgA responses were significantly associated with recent history of COVID-19 like symptoms. Salivary IgG also tolerated temperature and detergent pre-treatments. Conclusions: Unlike SARS-CoV-2 salivary IgA that appeared short-lived, the specific IgG in saliva appears stable even after mild COVID-19 as noted for blood serology. The non-invasive saliva-based SARS-Cov-2 antibody testing with self-collection at homes may thus serve as a complementary alternative to conventional blood serology.

Since glaucoma is a serious health problem, numerous therapeutics are being developed to reduce Intraocular Pressure (IOP) as the only modifiable factor of all glaucoma symptoms. IOP-lowering agents are divided into six groups, each of which has a specific mechanism of action and side effects, which are the focus of this article and are explained in detail. All the mentioned agents are formulated as eye drops. However, as conventional topical eye drops have significant disadvantages, of which poor bioavailability and patient noncompliance are the main, novel approaches to designing their drug delivery systems were used and briefly presented in this review. Review Article Rahić et al.; OR, 14(2): 17-33, 2021; Article no.OR.66197 18 Graphical Abstract

Regional integration into the Gulf Cooperation Council has enabled respective countries to effectively participate in global supply chains. To ensure effective integration of this region into global supply chains, logistics operations are a very important determinant. The aim of this study was to assess logistical performances of GCC countries, and to identify which country has the best conditions for establishing a regional logistic center. For this study, we used relevant data from Logistics Performance Index (LPI) developed by the World Bank. The research was conducted using a hybrid multicriteria approach based on the CRITIC and MABAC methods. The findings of this study indicate that the United Arab Emirates has the best conditions for establishing a regional logistics center. This study also releveled the areas of logistics in which other GCC countries should make an improvement to improve their logistical performance.