Temporal coupled-mode theory for thermal emission from multiple arbitrarily coupled resonators

Controlling the spectral response of thermal emitters has become increasingly important for a range of energy and sensing applications. Conventional approaches to achieving arbitrary spectrum selectivity in photonic systems have entailed combining multiple resonantly emissive elements together to achieve a range of spectral profiles through numerical optimization, with a universal theoretical framework lacking. Here, we develop a temporal coupled mode theory for thermal emission from multiple, arbtirarily-coupled resonators. We validate our theory against numerical simulations of complex two- and three-dimensional nanophotonic thermal emitters, highlighting the anomalous thermal emission spectra that can emerge when multiple resonators with arbitrary properties couple to each other with varying strengths

A 3D-printed microfluidic-enabled hollow microneedle architecture for transdermal drug delivery.

Embedding microfluidic architectures with microneedles enables fluid management capabilities that present new degrees of freedom for transdermal drug delivery. To this end, fabrication schemes that can simultaneously create and integrate complex millimeter/centimeter-long microfluidic structures and micrometer-scale microneedle features are necessary. Accordingly, three-dimensional (3D) printing techniques are suitable candidates because they allow the rapid realization of customizable yet intricate microfluidic and microneedle features. However, previously reported 3D-printing approaches utilized costly instrumentation that lacked the desired versatility to print both features in a single step and the throughput to render components within distinct length-scales. Here, for the first time in literature, we devise a fabrication scheme to create hollow microneedles interfaced with microfluidic structures in a single step. Our method utilizes stereolithography 3D-printing and pushes its boundaries (achieving print resolutions below the full width half maximum laser spot size resolution) to create complex architectures with lower cost and higher print speed and throughput than previously reported methods. To demonstrate a potential application, a microfluidic-enabled microneedle architecture was printed to render hydrodynamic mixing and transdermal drug delivery within a single device. The presented architectures can be adopted in future biomedical devices to facilitate new modes of operations for transdermal drug delivery applications such as combinational therapy for preclinical testing of biologic treatments

Allogeneic morphogenetic protein vs. recombinant human bone morphogenetic protein-2 in lumbar interbody fusion procedures: a radiographic and economic analysis

BACKGROUND: Since the introduction of rhBMP-2 (Infuse®) in 2002, surgeons have had an alternative substitute to autograft and its related donor site morbidity. Recently, the prevalence of reported adverse events and complications related to the use of rhBMP-2 has raised many ethical and legal concerns for surgeons. Additionally, the cost and decreasing reimbursement landscape of rhBMP-2 use have required identification of a viable alternative. Osteo allogeneic morphogenetic protein (OsteoAMP®) is a commercially available allograft-derived growth factor rich in osteoinductive, angiogenic, and mitogenic proteins. This study compares the radiographic fusion outcomes between rhBMP-2 and OsteoAMP allogeneic morphogenetic protein in lumbar interbody fusion spine procedures. METHODS: Three hundred twenty-one (321) patients from three centers underwent a transforaminal lumbar interbody fusion (TLIF) or lateral lumbar interbody fusion (LLIF) procedure and were assessed by an independent radiologist for fusion and radiographically evident complications. The independent radiologist was blinded to the intervention, product, and surgeon information. Two hundred and twenty-six (226) patients received OsteoAMP with autologous local bone, while ninety-five (95) patients received Infuse with autologous local bone. Patients underwent radiographs (x-ray and/or CT) at standard postoperative follow-up intervals of approximately 1, 3, 6, 12, and 18 months. Fusion was defined as radiographic evidence of bridging across endplates, or bridging from endplates to interspace disc plugs. Osteobiologic surgical supply costs were also analyzed to ascertain cost differences between OsteoAMP and rhBMP-2. RESULTS: OsteoAMP produced higher rates of fusion at 6, 12, and 18 months (p ≤ 0.01). The time required for OsteoAMP to achieve fusion was approximately 40% less than rhBMP-2 with approximately 70% fewer complications. Osteobiologic supply costs were 80.5% lower for OsteoAMP patients (73.7% lower per level) than for rhBMP-2. CONCLUSIONS: Results of this study indicate that OsteoAMP is a viable alternative to rhBMP-2 both clinically and economically when used in TLIF and LLIF spine procedures

DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms

In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural network can rapidly identify solutions near the global optimum with a given dataset's design space, an iterative optimization algorithm can further refine the solution and overcome dataset limitations. Furthermore, such hybrid ML-optimization methodologies can reduce computational costs and expedite the discovery of novel electromagnetic components. However, existing hybrid ML-optimization methods have yet to optimize across both materials and geometries in a single integrated and user-friendly environment. In addition, due to the challenge of acquiring large datasets for ML, as well as the exponential growth of isolated models being trained for photonics design, there is a need to standardize the ML-optimization workflow while making the pre-trained models easily accessible. Motivated by these challenges, here we introduce DeepAdjoint, a general-purpose, open-source, and multi-objective "all-in-one" global photonics inverse design application framework which integrates pre-trained deep generative networks with state-of-the-art electromagnetic optimization algorithms such as the adjoint variables method. DeepAdjoint allows a designer to specify an arbitrary optical design target, then obtain a photonic structure that is robust to fabrication tolerances and possesses the desired optical properties - all within a single user-guided application interface. Our framework thus paves a path towards the systematic unification of ML and optimization algorithms for photonic inverse design

Antineoplastic effects of an Aurora B kinase inhibitor in breast cancer

<p>Abstract</p> <p>Background</p> <p>Aurora B kinase is an important mitotic kinase involved in chromosome segregation and cytokinesis. It is overexpressed in many cancers and thus may be an important molecular target for chemotherapy. AZD1152 is the prodrug for AZD1152-HQPA, which is a selective inhibitor of Aurora B kinase activity. Preclinical antineoplastic activity of AZD1152 against acute myelogenous leukemia, multiple myeloma and colorectal cancer has been reported. However, this compound has not been evaluated in breast cancer, the second leading cause of cancer deaths among women.</p> <p>Results</p> <p>The antineoplastic activity of AZD1152-HQPA in six human breast cancer cell lines, three of which overexpress HER2, is demonstrated. AZD1152-HQPA specifically inhibited Aurora B kinase activity in breast cancer cells, thereby causing mitotic catastrophe, polyploidy and apoptosis, which in turn led to apoptotic death. AZD1152 administration efficiently suppressed the tumor growth in a breast cancer cell xenograft model. In addition, AZD1152 also inhibited pulmonary metastatic nodule formation in a metastatic breast cancer model. Notably, it was also found that the protein level of Aurora B kinase declined after inhibition of Aurora B kinase activity by AZD1152-HQPA in a time- and dose-dependent manner. Investigation of the underlying mechanism suggested that AZD1152-HQPA accelerated protein turnover of Aurora B via enhancing its ubiquitination.</p> <p>Conclusions</p> <p>It was shown that AZD1152 is an effective antineoplastic agent for breast cancer, and our results define a novel mechanism for posttranscriptional regulation of Aurora B after AZD1152 treatment and provide insight into dosing regimen design for this kinase inhibitor in metastatic breast cancer treatment.</p

Exposures to respirable air particles in urban microenvironments and effects of background levels on cardiorespiratory symptoms

Epidemiological evidence has shown that increased levels of respirable particulate air pollution leads to adverse cardiorespiratory health effects although the exact mechanism of damage is unknown. In the UK the largest single source of respirable air particulates is road transport. Currently the background levels of respirable air particulates are measured by stationary monitoring stations. However, traffic volumes often vary considerably within a city and hotspots of densely trafficked areas may give rise to microenvironments with increased respirable particulate levels. The primary objective of this thesis was to investigate whether a high density of local motor traffic would give rise to microenvironments with increased levels of respirable air particulates. A selection of streets in Cardiff city were allocated into exposed and control group according to traffic volumes. Levels of respirable air particulates were measured for each residential location both indoor and outdoor, and individual residents provided blood, urine, and hair samples for the analysis of trace elements, which may serve as biomarkers of exposure to air particulates from motor vehicles. Results showed that for both indoor and outdoor respirable air particulate concentrations, the levels were found to be higher in exposed areas than controls, and there was a moderately high correlation between indoor and outdoor concentrations. However, the study failed to demonstrate any differential uptake of trace elements as reflected by the lack of differences in the levels of biomarkers in the biological samples of subjects residing in different exposure areas. A separate study was carried out to investigate whether short-term changes in respirable air particulate levels would lead to acute exacerbation of disease symptoms in individuals with asthma, chronic respiratory diseases excluding asthma, and chronic cardiac diseases. Subjects were recruited through specialist hospital outpatient clinics located in South Wales and disease specific questionnaires were sent out during different episodes of respirable particulate air pollution. Results showed that symptoms of most subjects were not affected by short-term changes in air particulate levels, although individuals with more severe asthma and cardiac disease appeared to have benefited relatively more from lower levels of respirable air particulates than those with less severe disease symptoms as well as those suffering from chronic respiratory disease

Metacognition in human decision-making: confidence and error monitoring

People are capable of robust evaluations of their decisions: they are often aware of their mistakes even without explicit feedback, and report levels of confidence in their decisions that correlate with objective performance. These metacognitive abilities help people to avoid making the same mistakes twice, and to avoid overcommitting time or resources to decisions that are based on unreliable evidence. In this review, we consider progress in characterizing the neural and mechanistic basis of these related aspects of metacognition—confidence judgements and error monitoring—and identify crucial points of convergence between methods and theories in the two fields. This convergence suggests that common principles govern metacognitive judgements of confidence and accuracy; in particular, a shared reliance on post-decisional processing within the systems responsible for the initial decision. However, research in both fields has focused rather narrowly on simple, discrete decisions—reflecting the correspondingly restricted focus of current models of the decision process itself—raising doubts about the degree to which discovered principles will scale up to explain metacognitive evaluation of real-world decisions and actions that are fluid, temporally extended, and embedded in the broader context of evolving behavioural goals