The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Infectious Diseases Society of America.

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The World Bank Group

Efficacy of antiseptic-impregnated central venous catheters in preventing catheter-related bloodstream infection. A Meta-analysis

During the past two decades, interleukin-12 (IL-12) has emerged as one of the most potent cytokines in mediating antitumor activity in a variety of preclinical models. Through pleiotropic effects on different immune cells that form the tumor microenvironment, IL-12 establishes a link between innate and adaptive immunity that involves different immune effector cells and cytokines depending on the type of tumor or the affected tissue. The robust antitumor response exerted by IL-12, however, has not yet been successfully translated into the clinics. The majority of clinical trials involving treatment with IL-12 failed to show sustained antitumor responses and were associated to toxic side effects. Here we discuss the therapeutic effects of IL-12 from preclinical to clinical studies, and will highlight promising strategies to take advantage of the antitumor activity of IL-12 while limiting adverse effects.

/pdf/new-insights-into-il-12-mediated-tumor-suppression-d4cnsxd2ev.pdf

New insights into IL-12-mediated tumor suppression

A Bioinformatic Analysis of Integrative Mobile Genetic Elements Highlights Their Role in Bacterial Adaptation

The study is an analysis of baseline data from a pilot psychosocial support intervention for HIV-affected youth and their caregivers in Haiti Six sites in Haiti's Central Department affiliated with Partners In Health/Zanmi Lasante (PIH/ZL) and the Haitian Ministry of Health were included Participants were recruited from a list of HIV-positive patients receiving care at PIH/ZL The baseline questionnaire was administered from February 2006 to January 2007 with HIV-affected youth (n = 492), ages 10-17, and their caregivers (n = 330) According to findings at baseline, the youth reported high levels of anxiety, including constant fidgeting (86%), restlessness (83%), and worrying a lot (56%) Their parents/caregivers also reported a high level of depressive symptoms, such as low energy (73%), feeling everything is an effort (71%), and sadness (69%) Parents' depressive symptoms were positively associated with their children's psychological symptoms (odds ratio [OR] =16-24) and psychosocial functioning (OR =16 according to parental report) The significant levels of anxiety and depression observed among HIV-affected youth and their caregivers suggest that psychosocial interventions are needed among HIV-affected families in central Haiti and other high HIV burden areas The results suggest that a family-focused approach to service provision may be beneficial, possibly improving quality of life, as well as psychosocial and physical health-related outcomes among HIV-affected youth and their caregivers, particularly HIV-positive parents

Psychosocial functioning among HIV-affected youth and their caregivers in Haiti: implications for family-focused service provision in high HIV burden settings.

Graph representation learning in biomedicine and healthcare

Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks However, despite the proliferation of the methods and their success, prevailing Graph Neural Networks (GNNs) neglect subgraphs, rendering subgraph prediction tasks challenging to tackle in many impactful applications Further, subgraph prediction tasks present several unique challenges: subgraphs can have non-trivial internal topology, but also carry a notion of position and external connectivity information relative to the underlying graph in which they exist Here, we introduce SubGNN, a subgraph neural network to learn disentangled subgraph representations We propose a novel subgraph routing mechanism that propagates neural messages between the subgraph's components and randomly sampled anchor patches from the underlying graph, yielding highly accurate subgraph representations SubGNN specifies three channels, each designed to capture a distinct aspect of subgraph topology, and we provide empirical evidence that the channels encode their intended properties We design a series of new synthetic and real-world subgraph datasets Empirical results for subgraph classification on eight datasets show that SubGNN achieves considerable performance gains, outperforming strong baseline methods, including node-level and graph-level GNNs, by 198% over the strongest baseline SubGNN performs exceptionally well on challenging biomedical datasets where subgraphs have complex topology and even comprise multiple disconnected components

Subgraph Neural Networks

To investigate the value of prospectively ECG-triggered coronary CT angiography (CCTA) for lean patients with body mass index (BMI) ≤ 23 kg/m2 using 70 kVp and high-level volume-based adaptive statistical iterative reconstruction (ASiR-V) algorithm on a 16-cm wide-detector CT system for reducing both radiation and contrast doses in comparison with the conventional 100-kVp protocol. Thirty patients (group A) were prospectively enrolled to undergo 70-kVp CCTA on a 16-cm wide-detector CT scanner with noise index (NI) of 36 HU and at weight-dependent contrast dose rate of 16 mg I/kg/s for 9-s injection. Images were reconstructed with 80% ASiR-V. Radiation dose, contrast dose, and image quality were statistically compared with 30 patients (group B) in database with matching BMI who underwent conventional 100-kVp CCTA with NI of 25 HU, and at 25 mg I/kg/s rate for 10-s injection and reconstructed with 60% ASiR-V. There was no significant difference in patient demographics between the two groups (all p > 0.05). The two groups also had similar mean CT values and contrast-noise ratio (CNR) and subjective image quality (all p > 0.05). However, group A with 70 kVp reduced the effective dose by 75.3% compared with group B (0.43 ± 0.20 mSv vs. 1.74 ± 1.01 mSv, p < 0.001), and required 42.4% less contrast dose than group B (22.46 ± 2.94 ml vs. 38.99 ± 5.10 ml, p < 0.001). Prospectively ECG-triggered CCTA using 70 kVp and high-level ASiR-V on a 16-cm wide-detector CT system provides diagnostic images with substantial reduction in both radiation and contrast doses for patients with BMI ≤ 23 kg/m2 compared to the conventional 100-kVp protocol. • 70-kVp CCTA produces excellent images at sub-millisievert radiation.
 • 70-kVp CCTA reduces both radiation and contrast doses over conventional protocol.
 • Achieving low-dose CCTA with combined low kVp and high-level ASIR-V.

Reducing both radiation and contrast doses in coronary CT angiography in lean patients on a 16-cm wide-detector CT using 70 kVp and ASiR-V algorithm, in comparison with the conventional 100-kVp protocol

Michelle Li

Papers

A Bioinformatic Analysis of Integrative Mobile Genetic Elements Highlights Their Role in Bacterial Adaptation

Psychosocial functioning among HIV-affected youth and their caregivers in Haiti: implications for family-focused service provision in high HIV burden settings.

Graph representation learning in biomedicine and healthcare

Subgraph Neural Networks

Reducing both radiation and contrast doses in coronary CT angiography in lean patients on a 16-cm wide-detector CT using 70 kVp and ASiR-V algorithm, in comparison with the conventional 100-kVp protocol