Illusions of gunk

The possibility of gunk has been used to argue against mereological nihilism. This paper explores two responses on the part of the microphysical mereological nihilist: (1) the contingency defence, which maintains that nihilism is true of the actual world; but that at other worlds, composition occurs; (2) the impossibility defence, which maintains that nihilism is necessary true, and so gunk worlds are impossible. The former is argued to be ultimately unstable; the latter faces the explanatorily burden of explaining the illusion that gunk is possible. It is argued that we can discharge this burden by focussing on the contingency of the microphysicalist aspect of microphysical mereological nihilism. The upshot is that gunk-based arguments against microphysical mereological nihilism can be resisted

Current status of gene therapy for breast cancer: progress and challenges

Breast cancer is characterized by a series of genetic mutations and is therefore ideally placed for gene therapy intervention. The aim of gene therapy is to deliver a nucleic acid-based drug to either correct or destroy the cells harboring the genetic aberration. More recently, cancer gene therapy has evolved to also encompass delivery of RNA interference technologies, as well as cancer DNA vaccines. However, the bottleneck in creating such nucleic acid pharmaceuticals lies in the delivery. Deliverability of DNA is limited as it is prone to circulating nucleases; therefore, numerous strategies have been employed to aid with biological transport. This review will discuss some of the viral and nonviral approaches to breast cancer gene therapy, and present the findings of clinical trials of these therapies in breast cancer patients. Also detailed are some of the most recent developments in nonviral approaches to targeting in breast cancer gene therapy, including transcriptional control, and the development of recombinant, multifunctional bio-inspired systems. Lastly, DNA vaccines for breast cancer are documented, with comment on requirements for successful pharmaceutical product development

Delivery of nucleic acids for cancer gene therapy: overcoming extra- and intra-cellular barriers

The therapeutic potential of cancer gene therapy has been limited by the difficulty of delivering genetic material to target sites. Various biological and molecular barriers exist which need to be overcome before effective nonviral delivery systems can be applied successfully in oncology. Herein, various barriers are described and strategies to circumvent such obstacles are discussed, considering both the extracellular and intracellular setting. Development of multifunctional delivery systems holds much promise for the progression of gene delivery, and a growing body of evidence supports this approach involving rational design of vectors, with a unique molecular architecture. In addition, the potential application of composite gene delivery platforms is highlighted which may provide an alternative delivery strategy to traditional systemic administration. </jats:p

Multifunctional Delivery Systems for Cancer Gene Therapy

This chapter examines key concepts with respect to cancer gene therapy and the current issues with respect to non-viral delivery. The biological and molecular barriers that need to be overcome before effective non-viral delivery systems can be appropriately designed for oncology applications are highlighted and ways to overcome these are discussed. Strategies developed to evade the immune response are also described and targeted gene delivery is examined with the most effective strategies highlighted. Finally, this chapter proposes a new way forward based on a growing body of evidence that supports a multifunctional delivery approach involving the creation of vectors, with a unique molecular architecture designed using a bottom-up approach

Stop Sticks: Reducing the Rist of Needlestick Injuries in Paramedic Practice

At any moment while performing patient care duties, paramedics are at risk of exposure to blood and infection from blood-borne pathogens such as human immunodeficiency virus (HIV), which causes AIDS, hepatitis B virus (HBV), and hepatitis C virus (HCV). Prevention of needle stick injuries (NSI) is becoming more feasible with technology and engineering advancement

The Case for Comparability

We argue that all gradable expressions in natural language obey a principle that we call Comparability: if x and y are both F to some degree, then either x is at least as F as y or y is at least as F as x. This principle has been widely rejected among philosophers, especially by ethicists, and its falsity has been claimed to have important normative implications. We argue that Comparability is needed to explain the goodness of several patterns of inference that seem manifestly valid, that the purported failures of Comparability would have absurd consequences, and that the influential arguments against Comparability are less compelling than they may have initially seemed

Deep learning applications in coronary anatomy imaging:a systematic review and meta-analysis

Background: The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging. Methods: The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach. Results: A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496). Conclusions: Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.</p

The Impact of Climate on Economic and Financial Cycles: A Markov-switching Panel Approach

This paper examines the impact of climate shocks on 13 European economies analysing jointly business and financial cycles, in different phases and disentangling the effects for different sector channels. A Bayesian Panel Markov-switching framework is proposed to jointly estimate the impact of extreme weather events on the economies as well as the interaction between business and financial cycles. Results from the empirical analysis suggest that extreme weather events impact asymmetrically across the different phases of the economy and heterogeneously across the EU countries. Moreover, we highlight how the manufacturing output, a component of the industrial production index, constitutes the main channel through which climate shocks impact the EU economies

Power systems' performance under high renewables' penetration rates: a natural experiment due to the COVID-19 demand shock

COVID-19 lockdowns make it possible to investigate the extent to which an unprecedented increase in renewables' penetration may have brought unexpected limitations and vulnerabilities of current power systems to the surface. We empirically investigate how power systems in five European countries have dealt with this unexpected shock, drastically changing electricity load, the scheduling of dispatchable generation technologies, electricity day-ahead wholesale prices, and balancing costs. We find that low-cost dispatchable generation from hydro and nuclear sources has fulfilled most of the net-load even during peak hours, replacing more costly fossil-based generation. In Germany, the UK, and Spain coal power plants stood idle, while gas-fired generation has responded in heterogeneous ways across power systems. Falling operational costs of generators producing at the margin and lower demand, both induced by COVID-19 lockdowns, have significantly decreased wholesale prices. Balancing and other ancillary services' markets have provided the flexibility required to respond to the exceptional market conditions faced by the grid. Balancing costs for flexibility services have increased heterogeneously across countries, while ancillary markets' costs, measured only in the case of Italy, have increased substantially. Results provide valuable evidence on current systems' dynamics during high renewables' shares and increased demand volatility. New insights into the market changes countries will be facing in the transition towards a clean, secure, and affordable power system are offered

THAWS: automated design and deployment of heterogeneous wireless sensor networks

This research focuses on the design and implementation of a tool to speed-up the development and deployment of heterogeneous wireless sensor networks. The THAWS (Tyndall Heterogeneous Automated Wireless Sensors) tool can be used to quickly create and configure application-specific sensor networks, based on a list of application requirements and constraints. THAWS presents the user with a choice of options, in order to gain this information on the functionality of the network. With this information, THAWS uses code generation techniques to create the necessary code from pre-written templates and well-tested, optimized software modules from a library, which includes an implementation of novel plug-and-play sensor interface. These library modules can also be modified at the code generation stage. The application code and necessary library modules are then automatically compiled to form binary instruction files for each node in the network. The binary instruction files then wirelessly propagate through the network, and reprogram the nodes. This completes the task of targeting the wireless network towards a specific sensing application. THAWS is an adaptable tool that works with both homogeneous and heterogeneous networks built from wireless sensor nodes that have been developed in the Tyndall National Institute. Its advantage over traditional methods of WSN development is simplification of development