Contractual Forms in Islamic Finance Law and Islamic Inv. Co. of the Gulf (Bahamas) Ltd. v. Symphony Gems N.V. & Ors.: A First Impression of Islamic Finance

This Article focuses on the case of Islamic Investment Company of the Gulf (Bahamas) Ltd. v. Symphony Gems N.V. & Others (“Symphony Gems”). Symphony Gems is the first instance where a Western court of law ruled on an Islamic financial transaction. Symphony Gems illuminates the challenges and tensions within the industrial complex of Islamic finance as it seeks to exist and thrive in a commercial reality where the regulatory framework and its associated assumptions (both theoretical as well as those of commercial practice) differ markedly from those of Islamic law and the contemporary Islamic financial industry. The resulting transactions often deviate from the classical modes or forms upon which they are supposed to be based. From a conventional finance perspective, Islamic transactions can be criticized as being anomalous, inefficiently structured and obliquely documented. Symphony Gems arose out of the applicability of an Islamic financial contract known as a murabahah. This Article will explain both the conceptual basis and the contemporary usage of the murabahah contract and, more generally, the challenges of integrating Islamic financial concepts into the Anglo-American legal system that predominates the modern global economy. Murabahah contracts, simply stated, involve the sale of an item, through a middleman, in which the ultimate buyer is aware of the middleman\u27s costs in obtaining the item. As discussed later in this Article, murabahah contracts in contemporary practice closely approximate conventional financing mechanisms, particularly the economics underlying a similarly-profiled conventional commercial financing. This is why murabahah contracts are so popular. Therefore, it is not surprising that the first instance in which a Western court of law has examined and opined upon an Islamic financial contract involves a murabahah sale

Research Alert – A Case Study

This paper describes the SDI service, Research Alert, developed in-house at the Structural Engineering Research Centre (SERC) Library. The library provides print as well as electronic versions of the service. The alert service is provided to other institutions and companies as well. The database contains about 45,000 articles with abstracts

ReLayer: a free, online tool for extracting retinal thickness from cross-platform OCT images

Purpose: To describe and evaluate a free, online tool for automatically segmenting optical coherence tomography (OCT) images from different devices and computing summary measures such as retinal thickness. Methods: ReLayer (https://relayer.online) is an online platform to which OCT scan images can be uploaded and analyzed. Results can be downloaded as plaintext (.csv) files. The segmentation method includes a novel, 1-dimensional active contour model, designed to locate the inner limiting membrane, inner/outer segmentand retinal pigment epithelium. The method, designed for B-scans from Heidelberg Engineering Spectralis, was adapted for Topcon 3D OCT-2000 and OptoVue AngioVue. The method was applied to scans from healthy and pathological eyes, and was validated against segmentation by the manufacturers, the 39IOWA Reference Algorithms,and manual segmentation. Results: Segmentation of a B-scan took≤ 1 second. In healthy eyes, mean difference in retinal thickness from ReLayer and the reference standard was below the resolution of the Spectralis and 3D OCT-2000, and slightly above the resolution of the AngioVue. In pathological eyes, ReLayer performed similarly to IOWA (p=0.97) and better than Spectralis (p<0.001)

Cloud-based genomics pipelines for ophthalmology: Reviewed from research to clinical practice

Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings. Design: Review article exploring the potential for cloud-based genomic pipelines in eye hospitals. Methods: Narrative review of the literature relevant to clinical genomics and cloud computing, using PubMed and Google Scholar. A broad overview of these fields is provided, followed by key examples of their integration. Results: Cloud computing could benefit clinical genomics due to scalability of resources, potentially lower costs, and ease of data sharing between multiple institutions. Challenges include complex pricing of services, costs from mistakes or experimentation, data security, and privacy concerns. Conclusions and future perspectives: Clinical genomics is likely to become more routinely used in clinical practice. Currently this is delivered in highly specialist centers. In the future, cloud computing could enable delivery of clinical genomics services in non-specialist hospital settings, in a fast, cost-effective way, whilst enhancing collaboration between clinical and research teams

Mutual Effects of Hydrogen Bonding and Polymer Hydrophobicity on Ibuprofen Crystal Inhibition in Solid Dispersions with Poly(N-vinyl pyrrolidone) and Poly(2-oxazolines)

Poly(N-vinyl pyrrolidone) (PVP), poly(2-methyl-2-oxazoline) (PMOZ), poly(2-ethyl-2-oxazoline) (PEOZ), poly(2-n-propyl-2-oxazoline) (PnPOZ), and poly(2-isopropyl-2-oxazoline) (PiPOZ) were used to prepare solid dispersions with ibuprofen (IB), a model poorly-water soluble drug. Dispersions, prepared by solvent evaporation, were investigated using powder X-ray diffractometry, differential scanning calorimetry, and FTIR spectroscopy; hydrogen bonds formed between IB and all polymers in solid dispersions. PMOZ, the most hydrophilic polymer, showed the poorest ability to reduce or inhibit the crystallinity of IB. In contrast, the more hydrophobic polymers PVP, PEOZ, PnPOZ, and PiPOZ provided greater but similar abilities to reduce IB crystallinity, despite the differing polymer hydrophobicity and that PiPOZ is semi-crystalline. These results indicate that crystallinity disruption is predominantly due to hydrogen bonding between the drug molecules and the polymer. However, carrier properties affected drug dissolution, where PnPOZ exhibited lower critical solution temperature that inhibited the release of IB, whereas drug release from other systems was consistent with the degree of ibuprofen crystallinity within the dispersions

The Personal Genome Project-UK, an open access resource of human multi-omics data

Integrative analysis of multi-omics data is a powerful approach for gaining functional insights into biological and medical processes. Conducting these multifaceted analyses on human samples is often complicated by the fact that the raw sequencing output is rarely available under open access. The Personal Genome Project UK (PGP-UK) is one of few resources that recruits its participants under open consent and makes the resulting multi-omics data freely and openly available. As part of this resource, we describe the PGP-UK multi-omics reference panel consisting of ten genomic, methylomic and transcriptomic data. Specifically, we outline the data processing, quality control and validation procedures which were implemented to ensure data integrity and exclude sample mix-ups. In addition, we provide a REST API to facilitate the download of the entire PGP-UK dataset. The data are also available from two cloud-based environments, providing platforms for free integrated analysis. In conclusion, the genotype-validated PGP-UK multi-omics human reference panel described here provides a valuable new open access resource for integrated analyses in support of personal and medical genomics

Sequenceserver: A Modern Graphical User Interface for Custom BLAST Databases

Comparing newly obtained and previously known nucleotide and amino-acid sequences underpins modern biological research. BLAST is a well-established tool for such comparisons but is challenging to use on new data sets. We combined a user-centric design philosophy with sustainable software development approaches to create Sequenceserver, a tool for running BLAST and visually inspecting BLAST results for biological interpretation. Sequenceserver uses simple algorithms to prevent potential analysis errors and provides flexible text-based and visual outputs to support researcher productivity. Our software can be rapidly installed for use by individuals or on shared servers

SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease

PURPOSE: Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). DESIGN: Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. PARTICIPANTS: Moorfields Eye Hospital (MEH) dataset of 15 692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. METHODS: A StyleGAN2 model is trained on the IRD dataset to generate 512 × 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. MAIN OUTCOME MEASURES: We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ). RESULTS: An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating that copied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC = 0.86[0.85-88], RB-AUROC = 0.88[0.86-0.89], R-k = 0.51[0.49-0.53], and RB-k = 0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC = 0.86[0.85-87], S-k = 0.48[0.46-0.50]). CONCLUSIONS: Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references