Search

Abstract Background F-627 (efbemalenograstim alfa) is a novel long acting granulocyte colony-stimulating factor (G-CSF) that contains two human G-CSF fused to a human immunoglobulin G2 (hIgG2) -Fc fragment with a peptide linker. This studyevaluated the efficacy and safety of F-627, also known as efbemalenograstim alfa (Ryzneuta®) in reducing neutropenia compared with filgrastim (GRAN®). Methods This was a multicenter, randomized, open-label, active-controlled non-inferiority study. Two hundred thirty nine (239) patients were enrolled in thirteen centers and received the chemotherapy with epirubicin (100 mg/m2) and cyclophosphamide (600 mg/m2) on day 1 of each cycle for a maximum of four cycles. Patients were randomized to receive either a single 20 mg subcutaneous (s.c.) injection of F-627 on day 3 of each cycle or daily s.c. injection of filgrastim 5 µg/kg/d starting from day 3 of each cycle. The primary endpoint was the duration of grade 3 or 4 neutropenia in cycle 1. The safety profile was also evaluated. Results The mean (SD) duration of grade 3 or 4 neutropenia in cycle 1 was 0.68 (1.10) and 0.71 (0.95) days for the F-627 and the filgrastim groups, respectively. The Hodges-Lehmann estimate of the between-group median difference (F-627 vs filgrastim) in the duration of grade 3 or 4 neutropenia in cycle 1 was 0 day and the upper limit of the one-sided 97.5% CI was 0 day, which was within the prespecified non-inferiority margin of 1-day. Results for all efficacy endpoints in cycles 2 − 4 were consistent with the results in cycle 1, however a trend towards a lower incidence and a shorter duration of grade 3 or 4 neutropenia and grade 4 neutropenia was observed in the F-627 group compared with the filgrastim group. The ANC nadir in the F-627 group was significantly higher than that in the filgrastim group in each cycle. A single fixed dose of F-627 was well tolerated and as safe as standard daily filgrastim. Conclusions A single fixed dose of 20 mg of F-627 in each cycle was as safe and effective as a daily dose of filgrastim 5 µg/kg/d in reducing neutropenia and its complications in patients who received four cycles of EC. Trial registration ClinicalTrials.gov: NCT04174599, on 22/11/2019.

Summary: Background: Children from racial and ethnic minority groups are at greater risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it is unclear whether they have increased risk for post-acute sequelae of SARS-CoV-2 (PASC). Our objectives were to assess whether the risk of respiratory and neurologic PASC differs by race/ethnicity and social drivers of health. Methods: We conducted a retrospective cohort study of individuals

Objectives The need for Trusted Research Environments (TREs) is clear. Several influential reports have highlighted that personal or sensitive data which have been collected for operational, commercial or governmental reasons need to be managed securely in an environment that encourages best practices. TREs are designed to enable only authorised projects and researchers access to sensitive data whilst minimising risk of data exposure. Yet the TRE landscape has grown organically over at least the last decade resulting in heterogeneous environments, making it harder for data to be discovered, shared and used for public benefit. A baseline specification for TREs is required. Approach We engaged with around 60 organisations covering government, industry, health and academia through regular, online, shared spaces or “Collaboration Cafés”. We also embedded the public voice within the project by having members of the public within the research team and developed a series of workshops to include and reflect their input to the architecture. The whole project was developed in the open to instil trustworthiness. Results The SATRE project has produced a reference TRE architecture and implementation through significant engagement with the UK TRE community. This baseline specification for UK TREs includes a set of four pillars, 28 capabilities and 160 statements. Conclusions and Implications An open and transparent TRE specification and architecture for a wide range of stakeholders has been developed. TREs can now use the SATRE specification to evaluate their environments against a common baseline.

Though we spend a significant amount of time in indoor and built environments as general occupants of residential or commercial spaces, we do not necessarily know how the heating, cooling, and ventilation services work in our occupied spaces. As the mechanical systems of buildings become more complex for energy saving and better indoor air quality, it is beneficial for occupants to learn more their built environment so that they can cooperate effectively for the building’s performance. In this context, the purpose of this research is to develop and evaluate how virtual reality (VR) technology can support occupants in understanding their built environment. An educational building on campus was selected for the development as it provides familiar spaces for potential participants in this research. This research was carried out in two stages. In Stage One, we, as researchers in mechanical engineering, explored the workflow for VR development and developed VR tours for four spaces: a classroom, an auditorium, a conference room, and a mechanical room. In Stage Two, we conducted a survey study to examine the VR experience from the perspective of users. In this survey study, we recruited 34 participants from engineering students/graduates, industry participants, and a sustainability group. The participants generally indicated a positive experience with the VR tours, although the quiz scores on the VR content were weak. From our reflection, we consider that positive and effective VR experiences for the education of the built environment require collaboration from three domains: (1) mechanical systems of buildings, (2) VR technology, and (3) pedagogy.

Objectives The renal nephrometry score uses imaging characteristics such as lesion diameter, location, and proximity to hilar vessels to categorize renal masses by complexity for preoperative planning. These characteristics may also be used to determine the best approach to targeted renal biopsy. This study was conducted to investigate the impact of renal lesion characteristics as measured by the renal nephrometry score on the choice of modality used for performing a targeted renal lesion biopsy and increasing the chance of yielding a diagnostic biopsy. Materials and Methods All targeted computed tomography (CT)/ultrasound-guided renal biopsies performed by our radiology department from January 2017 to February 2020 were reviewed. Radiological characteristics and pathological outcomes were recorded with data on lesion size/ side, location in craniocaudal/anterior–posterior planes, endophytic/exophytic/mixed nature, and skin-lesion distance. Statistical Analysis Chi-squared tests, multivariate analysis, and t-tests were used in this study. Results Of the 145 consecutive patients included in the study, 86.2% (125/145) biopsies were diagnostic. About 54.5% (79/145) biopsies were ultrasound-guided, while 45.5% (66/145) were CT-guided. About 62.1% (90/145) biopsies revealed renal cell carcinoma. The highest rate of diagnostic biopsy was in the exophytic, laterally positioned mass either entirely below lower polar or above upper polar line. Ultrasound was preferred for lesions under 4cm and 4 to 7cm (p = 0.06). CT was used for anterior lesions and ultrasound for posterior and lateral lesions (p

Traditional engineering education (Eng. Ed) has received criticism for restricting student learning and experiences to practical skills development while ignoring the significance of fostering cognitive skills that encourage higher order thinking, criticality, and self-reflexivity. Imaginative education (IE) has emerged as a consideration for replacing such skills focused engineering curricula with interactive, engaging, and student-centered pedagogical approaches. However, existing literature on the topic as well as Egan's (1997) own explanation of the five stages of understanding (somatic, mythic, romantic, philosophic, and ironic) are mainly focused on K-12 contexts, leaving limited resources and insights for higher education contexts. This calls for theoretical and practical expansion of the topic where development and implementation of IE-informed Eng. Ed for adult engineering students remain the focus. To respond to this call, this conceptual paper focuses on two main points. First, it attempts to unpack the theoretical underpinnings of the five stages of IE to understand what each stage means for educators and learners in higher education engineering contexts. Second, after outlining the challenges that traditional Eng. Ed is facing in a globalized world today and the initiatives from the field to address them, it discusses the promises IE can bring to make Eng. Ed more effective, inclusive, and relevant. Overall, the intention in this paper is to turn to the theoretical tensions that may emerge when considering IE as an approach to re-imagine and expand Eng. Ed.

In incremental eco-design improvements, design engineers attempt to modify an existing product via some eco-design measures to reduce the product’s environmental impacts (e.g. reduction of material usage and energy consumption). In this process, several design concepts can be proposed, and concept selection is required to allocate resources sensibly to promising design concepts only. In this context, the research purpose is to estimate the environmental impacts of each concept given the uncertainty of design information. In the proposed methodology, the fuzzy interval arithmetic is used to specify and propagate imprecise design information. Then, the centroid concept is applied to model different views of imprecision (i.e. pessimistic, balanced and optimistic) associated with fuzzy impact assessment. Accordingly, a decision scheme is developed to support concept selection and suggest the potential areas for further eco-design improvements. A coffee maker is used as an example to demonstrate the proposed methodology. Also, the Monte Carlo Simulation is applied for the same example to compare the numerical outcomes by the fuzzy interval approach.

This paper proposes the use of the statistics of similarity values to evaluate the clusterability or structuredness associated with a cell formation (CF) problem. Typically, the structuredness of a CF solution cannot be known until the CF problem is solved. In this context, this paper investigates the similarity statistics of machine pairs to estimate the potential structuredness of a given CF problem without solving it. One key observation is that a well-structured CF solution matrix has a relatively high percentage of high-similarity machine pairs. Then, histograms are used as a statistical tool to study the statistical distributions of similarity values. This study leads to the development of the U-shape criteria and the criterion based on the Kolmogorov-Smirnov test. Accordingly, a procedure is developed to classify whether an input CF problem can potentially lead to a well-structured or ill-structured CF matrix. In the numerical study, 20 matrices were initially used to determine the threshold values of the criteria, and 40 additional matrices were used to verify the results. Further, these matrix examples show that genetic algorithm cannot effectively improve the well-structured CF solutions (of high grouping efficacy values) that are obtained by hierarchical clustering (as one type of heuristics). This result supports the relevance of similarity statistics to preexamine an input CF problem instance and suggest a proper solution approach for problem solving.

The purpose of this paper is to propagate the input uncertainties of the degree-day method to estimate the building heating energy consumption as numerical intervals. While it is common to use average or expected values (e.g., Typical Meteorological Year) to address the input uncertainties, this practice can only yield the best estimates as single-point values without informing the possible range of variations. After classifying two types of uncertainty as weather variability and imprecision in the degree-day method, this paper proposes the adoption of fuzzy numbers and their arithmetic as the theoretical approach to handle uncertainty. As the degree-day method mainly involves elementary arithmetic (e.g., addition and multiplication), fuzzy number arithmetic can be directly applied to formally process numerical intervals. The proposed method is demonstrated and verified via a building example in Canada, and the interval results are comparable to the variation of heating energy consumption based on the data of outdoor ambient temperatures in 52 years.

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.

Abstract Animal models that accurately reflect COVID-19 are vital for understanding mechanisms of disease and advancing development of improved vaccines and therapeutics. Pigs are increasingly recognized as valuable models for human disease due to their genetic, anatomical, physiological, and immunological similarities to humans, and they present a more ethically viable alternative to non-human primates. However, pigs are not susceptible to SARS-CoV-2 infection which limits their utility as a model. To address this, we have developed transgenic pigs expressing human ACE2 that are susceptible to SARS-CoV-2 infection. Following challenge, clinical signs consistent with COVID-19, including fever, coughing and respiratory distress were observed, with virus replication detected in the nasal turbinates, trachea and lungs up to the study endpoint, seven days post-infection. Notably, examination of tissues revealed immunopathology in the lungs consistent with histological changes observed in fatal human COVID-19 cases. This study establishes human ACE2 transgenic pigs as a large animal model that accurately reflects many aspects of COVID-19 disease.

Abstract For general random tensor network states at large bond dimension, we prove that the integer Rényi reflected entropies (away from phase transitions) are determined by minimal triway cuts through the network. This generalizes the minimal cut description of bipartite entanglement for these states. A natural extrapolation away from integer Rényi parameters, suggested by the triway cut problem, implies the holographic conjecture S R = 2EW, where S R is the reflected entropy and EW is the entanglement wedge cross-section. Minimal triway cuts can be formulated as integer programs which cannot be relaxed to find a dual maximal flow/bit-thread description. This sheds light on the gap between the existence of tripartite entanglement in holographic states and the bipartite entanglement structure motivated by bit-threads. In particular, we prove that the Markov gap that measures tripartite entanglement is lower bounded by the integrality gap of the integer program that computes the triway cut.

Abstract The Auxin Response Factors (ARFs) family of transcription factors are the central mediators of auxin-triggered transcriptional regulation. Functionally different classes of extant ARFs operate as antagonistic auxin-dependent and -independent regulators. While part of the evolutionary trajectory to the present auxin response functions has been reconstructed, it is unclear how ARFs emerged, and how early diversification led to functionally different proteins. Here, we use in silico and in vivo analyses to revisit the molecular events that led to the origin and subsequent evolution of the ARFs. We reveal the shared origin of ARFs from preexisting domains, uncovering a protein fold homologous to the ARF DNA-binding fold in a conserved eukaryotic chromatin regulator. Building on this, we reconstruct the complete evolutionary history of ARFs, including the divergence events leading to the appearance of the ARF classes and defining the main molecular targets for their functional diversification. We derive a complete evolutionary trajectory that led to the emergence of the nuclear auxin signalling pathway.

Abstract Background Microglial activation is one hallmark of Alzheimer disease (AD) neuropathology but the impact of the regional interplay of microglia cells in the brain is poorly understood. We hypothesized that microglial activation is regionally synchronized in the healthy brain but experiences regional desynchronization with ongoing neurodegenerative disease. We addressed the existence of a microglia connectome and investigated microglial desynchronization as an AD biomarker. Methods To validate the concept, we performed microglia depletion in mice to test whether interregional correlation coefficients (ICCs) of 18 kDa translocator protein (TSPO)-PET change when microglia are cleared. Next, we evaluated the influence of dysfunctional microglia and AD pathophysiology on TSPO-PET ICCs in the mouse brain, followed by translation to a human AD-continuum dataset. We correlated a personalized microglia desynchronization index with cognitive performance. Finally, we performed single-cell radiotracing (scRadiotracing) in mice to ensure the microglial source of the measured desynchronization. Results Microglia-depleted mice showed a strong ICC reduction in all brain compartments, indicating microglia-specific desynchronization. AD mouse models demonstrated significant reductions of microglial synchronicity, associated with increasing variability of cellular radiotracer uptake in pathologically altered brain regions. Humans within the AD-continuum indicated a stage-depended reduction of microglia synchronicity associated with cognitive decline. scRadiotracing in mice showed that the increased TSPO signal was attributed to microglia. Conclusion Using TSPO-PET imaging of mice with depleted microglia and scRadiotracing in an amyloid model, we provide first evidence that a microglia connectome can be assessed in the mouse brain. Microglia synchronicity is closely associated with cognitive decline in AD and could serve as an independent personalized biomarker for disease progression.

Abstract Adaptive deep brain stimulation (aDBS) is an emerging advancement in DBS technology; however, local field potential (LFP) signal rate detection sufficient for aDBS algorithms and the methods to set-up aDBS have yet to be defined. Here we summarize sensing data and aDBS programming steps associated with the ongoing Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) pivotal trial (NCT04547712). Sixty-eight patients were enrolled with either subthalamic nucleus or globus pallidus internus DBS leads connected to a Medtronic PerceptTM PC neurostimulator. During the enrollment and screening procedures, a LFP (8–30 Hz, ≥1.2 µVp) control signal was identified by clinicians in 84.8% of patients on medication (65% bilateral signal), and in 92% of patients off medication (78% bilateral signal). The ADAPT-PD trial sensing data indicate a high LFP signal presence in both on and off medication states of these patients, with bilateral signal in the majority, regardless of PD phenotype.