Searching the World-Wide-Web using nucleotide and peptide sequences

*Background:* No approaches have yet been developed to allow instant searching of the World-Wide-Web by just entering a string of sequence data. Though general search engines can be tuned to accept ‘processed’ queries, the burden of preparing such ‘search strings’ simply defeats the purpose of quickly locating highly relevant information. Unlike ‘sequence similarity’ searches that employ dedicated algorithms (like BLAST) to compare an input sequence from defined databases, a direct ‘sequence based’ search simply locates quick and relevant information about a blunt piece of nucleotide or peptide sequence. This approach is particularly invaluable to all biomedical researchers who would often like to enter a sequence and quickly locate any pertinent information before proceeding to carry out detailed sequence alignment. 

*Results:* Here, we describe the theory and implementation of a web-based front-end for a search engine, like Google, which accepts sequence fragments and interactively retrieves a collection of highly relevant links and documents, in real-time. e.g. flat files like patent records, privately hosted sequence documents and regular databases. 

*Conclusions:* The importance of this simple yet highly relevant tool will be evident when with a little bit of tweaking, the tool can be engineered to carry out searches on all kinds of hosted documents in the World-Wide-Web.

*Availability:* Instaseq is free web based service that can be accessed by visiting the following hyperlink on the WWW
http://instaseq.georgetown.edu 
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Introduction of carrier interference to spread spectrum multiple access

This paper introduces a new scheme for spread spectrum multiple access. Like MC-CDMA, this scheme accomplishes spectral spreading by transmission of identical data over N carriers simultaneously. However, unlike any existing CDMA technique to date, this method supports user orthogonality not through the use of spreading codes (based on PN sequences), but rather through multiple carrier interference. Specifically, in this novel method, aptly named CIMA (carrier interference multiple access), the interference of multiple carriers enables user orthogonality or pseudo orthogonality based on user positioning in time. It is shown that CIMA supports simplified receiver structures for AWGN channels, and offers performance benefits in AWGN and fading environment

5′′-(4-Chloro­benzyl­idene)-4′-(4-chloro­phen­yl)-5-fluoro-1′,1′′-dimethyl­indoline-3-spiro-2′-pyrrolidine-3′-spiro-3′′-piperidine-2,4′′-dione

The piperidine ring of the title compound, C30H26Cl2FN3O2, adopts a twisted chair conformation. The pyrrolidine ring has a twisted envelope structure with the N atom at the flap [displaced by 0.592 (3) Å]. The fluoro­oxindole, chloro­phenyl and chloro­benzyl­idene groups are planar with r.m.s. deviations of 0.0348, 0.0048 and 0.0048 Å, respectively. The structure is stabilized by inter­molecular N—H⋯O hydrogen bonds

Federated Benchmarking of Medical Artificial Intelligence With MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform

Host lipidome and tuberculosis treatment failure

INTRODUCTION: Host lipids play important roles in tuberculosis (TB) pathogenesis. Whether host lipids at TB treatment initiation (baseline) affect subsequent treatment outcomes has not been well characterised. We used unbiased lipidomics to study the prospective association of host lipids with TB treatment failure. METHODS: A case–control study (n=192), nested within a prospective cohort study, was used to investigate the association of baseline plasma lipids with TB treatment failure among adults with pulmonary TB. Cases (n=46) were defined as TB treatment failure, while controls (n=146) were those without failure. Complex lipids and inflammatory lipid mediators were measured using liquid chromatography mass spectrometry techniques. Adjusted least-square regression was used to assess differences in groups. In addition, machine learning identified lipids with highest area under the curve (AUC) to classify cases and controls. RESULTS: Baseline levels of 32 lipids differed between controls and those with treatment failure after false discovery rate adjustment. Treatment failure was associated with lower baseline levels of cholesteryl esters and oxylipin, and higher baseline levels of ceramides and triglycerides compared to controls. Two cholesteryl ester lipids combined in a unique classifier model provided an AUC of 0.79 (95% CI 0.65–0.93) in the test dataset for prediction of TB treatment failure. CONCLUSIONS: We identified lipids, some with known roles in TB pathogenesis, associated with TB treatment failure. In addition, a lipid signature with prognostic accuracy for TB treatment failure was identified. These lipids could be potential targets for risk-stratification, adjunct therapy and treatment monitoring

MedPerf : Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform

Adjunctive rifampicin for Staphylococcus aureus bacteraemia (ARREST): a multicentre, randomised, double-blind, placebo-controlled trial.

BACKGROUND: Staphylococcus aureus bacteraemia is a common cause of severe community-acquired and hospital-acquired infection worldwide. We tested the hypothesis that adjunctive rifampicin would reduce bacteriologically confirmed treatment failure or disease recurrence, or death, by enhancing early S aureus killing, sterilising infected foci and blood faster, and reducing risks of dissemination and metastatic infection. METHODS: In this multicentre, randomised, double-blind, placebo-controlled trial, adults (≥18 years) with S aureus bacteraemia who had received ≤96 h of active antibiotic therapy were recruited from 29 UK hospitals. Patients were randomly assigned (1:1) via a computer-generated sequential randomisation list to receive 2 weeks of adjunctive rifampicin (600 mg or 900 mg per day according to weight, oral or intravenous) versus identical placebo, together with standard antibiotic therapy. Randomisation was stratified by centre. Patients, investigators, and those caring for the patients were masked to group allocation. The primary outcome was time to bacteriologically confirmed treatment failure or disease recurrence, or death (all-cause), from randomisation to 12 weeks, adjudicated by an independent review committee masked to the treatment. Analysis was intention to treat. This trial was registered, number ISRCTN37666216, and is closed to new participants. FINDINGS: Between Dec 10, 2012, and Oct 25, 2016, 758 eligible participants were randomly assigned: 370 to rifampicin and 388 to placebo. 485 (64%) participants had community-acquired S aureus infections, and 132 (17%) had nosocomial S aureus infections. 47 (6%) had meticillin-resistant infections. 301 (40%) participants had an initial deep infection focus. Standard antibiotics were given for 29 (IQR 18-45) days; 619 (82%) participants received flucloxacillin. By week 12, 62 (17%) of participants who received rifampicin versus 71 (18%) who received placebo experienced treatment failure or disease recurrence, or died (absolute risk difference -1·4%, 95% CI -7·0 to 4·3; hazard ratio 0·96, 0·68-1·35, p=0·81). From randomisation to 12 weeks, no evidence of differences in serious (p=0·17) or grade 3-4 (p=0·36) adverse events were observed; however, 63 (17%) participants in the rifampicin group versus 39 (10%) in the placebo group had antibiotic or trial drug-modifying adverse events (p=0·004), and 24 (6%) versus six (2%) had drug interactions (p=0·0005). INTERPRETATION: Adjunctive rifampicin provided no overall benefit over standard antibiotic therapy in adults with S aureus bacteraemia. FUNDING: UK National Institute for Health Research Health Technology Assessment