Positive selection and inactivation in the vision and hearing genes of cetaceans.

The transition to an aquatic lifestyle in cetaceans (whales and dolphins) resulted in a radical transformation in their sensory systems. Toothed whales acquired specialized high-frequency hearing tied to the evolution of echolocation, while baleen whales evolved low-frequency hearing. More generally, all cetaceans show adaptations for hearing and seeing underwater. To determine the extent to which these phenotypic changes have been driven by molecular adaptation, we performed large-scale targeted sequence capture of 179 sensory genes across the Cetacea, incorporating up to 54 cetacean species from all major clades as well as their closest relatives, the hippopotamuses. We screened for positive selection in 167 loci related to vision and hearing, and found that the diversification of cetaceans has been accompanied by pervasive molecular adaptations in both sets of genes, including several loci implicated in non-syndromic hearing loss (NSHL). Despite these findings, however, we found no direct evidence of positive selection at the base of odontocetes coinciding with the origin of echolocation, as found in studies examining fewer taxa. By using contingency tables incorporating taxon- and gene-based controls, we show that, while numbers of positively selected hearing and NSHL genes are disproportionately high in cetaceans, counts of vision genes do not differ significantly from expected values. Alongside these adaptive changes, we find increased evidence of pseudogenization of genes involved in cone-mediated vision in mysticetes and deep diving odontocetes

Molecular Mechanistic Explanation for the Spectrum of Cholestatic Disease Caused by the S320F Variant of ABCB4

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/hep.26970E.J.A. was funded by Barts and the London Charity award 458/1495. M.N. was funded by a Medical Research Council centenary award. M.R.R. was supported by funding from the Spanish Ministry of Science (Grant SAF2010-15517). The groups of K.J.L. and C.W. are supported by the Medical Research Council, UK (MC_U120088463) and Imperial College Healthcare NHS trust biomedical research centre, respectively

Case Report:False-negative HIV-1 polymerase chain reaction in a 15-month-old boy with HIV-1 subtype C infection

Polymerase chain reaction (PCR) testing is the gold standard for determining the HIV status in children <18 months of age. However, when clinical manifestations are not consistent with laboratory results, additional investigation is required. We report a 15-month-old HIV-exposed boy referred to our hospital after he had been admitted several times for infectious diseases. A rapid antibody test on the child was positive, while routine diagnostic HIV PCRs using the Roche COBAS Ampliprep/COBAS TaqMan HIV Qual Test were negative at 6 weeks, 6 months, 7 months and 15 months. In addition, the same PCR test performed on the HIV-infected mother was also negative. Alternative PCR and viral load assays using different primer sets detected HIV RNA or proviral DNA in both child and mother. Gag sequences from the child and his mother classified both infections as HIV-1 subtype C, with very rare mutations that may have resulted in PCR assay primer/probe mismatch. Consequently, the child was commenced on antiretroviral therapy and made a remarkable recovery. These findings indicate that more reliable PCR assays capable of detecting a wide range of HIV subtypes are desirable to circumvent the clinical problems created by false-negative PCR results

Online unit clustering in higher dimensions

We revisit the online Unit Clustering and Unit Covering problems in higher dimensions: Given a set of $n$ points in a metric space, that arrive one by one, Unit Clustering asks to partition the points into the minimum number of clusters (subsets) of diameter at most one; while Unit Covering asks to cover all points by the minimum number of balls of unit radius. In this paper, we work in $\mathbb{R}^d$ using the $L_\infty$ norm. We show that the competitive ratio of any online algorithm (deterministic or randomized) for Unit Clustering must depend on the dimension $d$. We also give a randomized online algorithm with competitive ratio $O(d^2)$ for Unit Clustering}of integer points (i.e., points in $\mathbb{Z}^d$, $d\in \mathbb{N}$, under $L_{\infty}$ norm). We show that the competitive ratio of any deterministic online algorithm for Unit Covering is at least $2^d$. This ratio is the best possible, as it can be attained by a simple deterministic algorithm that assigns points to a predefined set of unit cubes. We complement these results with some additional lower bounds for related problems in higher dimensions.Comment: 15 pages, 4 figures. A preliminary version appeared in the Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA 2017

e-Consent in UK academic-led clinical trials: current practice, challenges and the need for more evidence

BACKGROUND: During the COVID-19 pandemic, in-person healthcare visits were reduced. Consequently, trial teams needed to consider implementing remote methods for conducting clinical trials, including e-Consent. Although some clinical trials may have implemented e-Consent prior to the pandemic, anecdotes of uptake for this method increased within academic-led trials. When the increased use of this process emerged, representatives from several large academic clinical trial groups within the UK collaborated to discuss ways in which trialists can learn from one another when implementing e-Consent. METHODS: A survey of UKCRC-registered Clinical Trials Units (CTUs) was undertaken in April–June 2021 to understand the implementation of and their views on the use of e-Consent and experiences from the perspectives of systems programmers and quality assurance staff on the use of e-Consent. CTUs not using e-Consent were asked to provide any reasons/barriers (including no suitable trials) and any plans for implementing it in the future. Two events for trialists and patient and public involvement (PPI) representatives were then held to disseminate findings, foster discussion, share experiences and aid in the identification of areas that the academic CTU community felt required more research. RESULTS: Thirty-four (64%) of 53 CTUs responded to the survey, with good geographical representation across the UK. Twenty-one (62%) of the responding CTUs had implemented e-Consent in at least one of their trials, across different types of trials, including CTIMPs (Clinical Trial of Investigational Medicinal Product), ATIMPs (Advanced Therapy Medicinal Products) and non-CTIMPs. One hundred ninety-seven participants attended the two workshops for wide-ranging discussions. CONCLUSION: e-Consent is increasingly used in academic-led trials, yet uncertainties remain amongst trialists, patients and members of the public. Uncertainties include a lack of formal, practical guidance and a lack of evidence to demonstrate optimal or appropriate methods to use. We strongly encourage trialists to continue to share their own experiences of the implementation of e-Consent

Wheldone Revisited: Structure Revision via DFT-GIAO chemical shift calculations, 1,1-HD-ADEQUATE NMR Spectroscopy, and X-ray Crystallography Studies

Wheldone was reported recently as a fungal metabolite isolated from the co-culture of Aspergillus fischeri and Xylaria flabelliformis, and it displayed cytotoxic activity against breast, melanoma, and ovarian cancer cell lines. Initially, its structure was characterized as an unusual 5-methyl-bicyclo[5.4.0]undec-3,5-diene scaffold with a 2‑hydroxy-1-propanone side chain and a 3-(2-(1-hydroxyethyl)-2-methyl-2,5-dihydrofuran-3-yl)acrylic acid moiety. Upon further examination, minor inconsistencies in the data suggested the need for structural revision. Thus, the structure of wheldone has been revisited herein using an orthogonal experimental-computational approach, which combines 1,1-HD-ADEQUATE NMR experiments, DFT-GIAO chemical shift calculations, and single crystal X-ray diffraction (SCXRD) analysis of a semi-synthetic p‑bromobenzylamide derivative, formed via a Steglich-type reaction. The summation of these data, in conjunction with previously reported Mosher’s ester analysis, now permit the unequivocal assignment of both the structure and absolute configuration of the natural product

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks