The Role of Futureproofing in the Management of Infrastructural Assets

Ensuring long-term value from infrastructure is essential for a sustainable economy. In this context, futureproofing involves addressing two broad issues: i. Ensuring the ability of infrastructure to be resilient to unexpected or uncontrollable events e.g. extreme weather events; and ii. Ensuring the ability to adapt to required changes in structure and / or operations of the infrastructure in the future e.g. expansion of capacity, change in usage mode or volumes. Increasingly, in their respective roles, infrastructure designers/builders and owners/operators are being required to develop strategies for futureproofing as part of the life cycle planning for key assets and systems that make up infrastructure. In this paper, we report on a preliminary set of studies aimed at exploring the following issues related to infrastructure / infrastructure systems: • What is intended by the futureproofing of infrastructural assets? • Why and when to futureproof critical infrastructure? • How can infrastructure assets and systems be prepared for uncertain futures? • How can futureproofing be incorporated into asset management practice? In order to seek answers to the above questions, the Cambridge Centre for Smart Infrastructure and Construction (CSIC) has conducted two industrial workshops bringing together leading practitioners in the UK infrastructure and construction sectors, along with government policy makers. This paper provides an initial summary of the findings from the workshops (part presentation, part working sessions), and proposes a simple framework for linking futureproofing into broader asset management considerations. To begin, an overview of futureproofing and motivate the need for futureproofing infrastructure assets is provided. Following this, an approach to futureproofing infrastructure portfolios is presented that organisations in the infrastructure sector can use. Key barriers to futureproofing are also presented before examining the ISO 55001 asset management standard to highlight the interplay between futureproofing and infrastructural asset management. Finally, different ways by which an effective futureproofing strategy can enhance the value of infrastructure are examined

Folate Nanoparticle Conjugates

The folate receptor is overexpressed on the surface of numerous cancer cell types including those of the breast, lung, kidney, ovary, and brain. Recent interest has exploded in the use of folate to deliver payloads and imaging agents to folate receptor positive cancer cells based on several positive clinical trials including phase III trials of EC-145, which is poised to become the first folate targeted, FDA approved drug. Given the success of EC-145 and numerous other agents in the pharmaceutical pipeline, there remains a great interest in the exploitation of this technology in the delivery of nanoscale agents to folate receptor positive cancer cells. In this review I will examine the current status and role of folate targeted nanoconjugates for both diagnostic imaging and therapy

TINOSPORA CORDIFOLIA AQUEOUS EXTRACT AMELIORATES THE SYSTEMIC INFECTION OF ASPERGILLUS FUMIGATUS IN BALB/C MICE

Objective: The present study was aimed to assess the antifungal activity of Tinospora cordifolia aqueous extract (TCAE) against Aspergillus fumigatus infection. Methods: TCAE was tested for in vitro antifungal activity against the isolates of A. fumigatus, Aspergillus flavus, and Aspergillus niger. To evaluate in vivo activity, various doses (10, 25, and 50 mg/kg) of TCAE were orally administered in A. fumigatus-infected mice for 7 days. The combination of prophylactic and therapeutic effect of TCAE was assessed by pre-treating the mice with 10 mg/kg of TCAE for 3 consecutive days before exposing them to A. fumigatus. Mice were treated with 10, 25, and 50 mg/kg doses of TCAE for 7 consecutive days’ post-A. fumigatus infection. The effectiveness of TCAE was evaluated by monitoring the survival rate and assessing the fungal burden in the kidney of the treated mice. Results: A. fumigatus-infected mice treated with TCAE at the doses of 25 and 50 mg/kg exhibited 50% and 20% survival rate, respectively, observed on day 40 post-treatment. Like to the survival data, the fungal burden was also found to be the lowest in the kidney of mice treated with TCAE at a dose of 50 mg/kg. The results showed that pre-treatment with TCAE (10 mg/kg) followed by post-infection treatment with 10, 25, and 50 mg/kg of TCAE for 7 days resulted in 40%, 50%, and 70% survival rate, respectively. Conclusions: These results suggest that TCAE may potentially be considered for its possible use in the treatment of the systemic infection of A. fumigatus

Genomic dissection of the 1994 Cronobacter sakazakii outbreak in a French neonatal intensive care unit

Background: Cronobacter sakazakii is a member of the genus Cronobacter that has frequently been isolated from powdered infant formula (PIF) and linked with rare but fatal neonatal infections such as meningitis and necrotising enterocolitis. The Cronobacter MLST scheme has reported over 400 sequence types and 42 clonal complexes; however C. sakazakii clonal complex 4 (CC4) has been linked strongly with neonatal infections, especially meningitis. There have been a number of reported Cronobacter outbreaks over the last three decades. The largest outbreak of C. sakazakii was in a neonatal intensive care unit (NICU) in France (1994) that lasted over 3 months and claimed the lives of three neonates. The present study used whole genome sequencing data of 26 isolates obtained from this outbreak to reveal their relatedness. This study is first of its kind to use whole genome sequencing data to analyse a Cronobacter outbreak. Methods: Whole genome sequencing data was generated for 26 C. sakazakii isolates on the Illumina MiSeq platform. The whole genome phylogeny was determined using Mugsy and RaxML. SNP calls were determined using SMALT and SAMtools, and filtered using VCFtools. Results: The whole genome phylogeny suggested 3 distant clusters of C. sakazakii isolates were associated with the outbreak. SNP typing and phylogeny indicate the source of the C. sakazakii could have been from extrinsic contamination of reconstituted infant formula from the NICU environment and personnel. This pool of strains would have contributed to the prolonged duration of the outbreak, which was up to 3 months. Furthermore 3 neonates were co-infected with C. sakazakii from two different genotype clusters. Conclusion: The genomic investigation revealed the outbreak consisted of an heterogeneous population of C. sakazakii isolates. The source of the outbreak was not identified, but probably was due to environmental and personnel reservoirs resulting in extrinsic contamination of the neonatal feeds. It also indicated that C. sakazakii isolates from different genotype clusters have the ability to co-infect neonates

SA-SVM based automated diagnostic System for Skin Cancer

Early diagnosis of skin cancer is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system aimed to save time and resources in the diagnostic process. Segmentation, feature extraction, pattern recognition, and lesion classification are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method H-FCM-LS. The underlying features which indicate the difference between melanoma and benign lesions are obtained through intensity, spatial/frequency and texture based methods. For classification purpose, self-advising SVM is adapted which showed improved classification rate as compared to standard SVM. The presented work also considers analyzed performance of linear and kernel based SVM on the specific skin lesion diagnostic problem and discussed corresponding findings. The best diagnostic rates obtained through the proposed method are around 90.5 %

Computer Aided Diagnostic Support System for Skin cancer: Review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided