In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use ...or to develop against an individual HIV strain. In this work we applied 'proteochemometric' modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 untested compound-mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62). Furthermore, dependent on the similarity of a new mutant to the training set, we could predict with high accuracy which compound will be most effective on a sequence with a previously unknown genotype. Hence, our models allow the evaluation of compound performance on untested sequences and the selection of the most promising leads for further preclinical research. The modeling concept is likely to be applicable also to other target families with genetic variability like other viruses or bacteria, or with similar orthologs like GPCRs.
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Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual ...patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions.
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The National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) houses the world's largest laser system, composed of 192 individual, 40-cm-aperture beamlines. The NIF laser ...routinely operates at ultraviolet (UV) fluences above 8 J/cm
2
, more than twice the (3ω only) damage threshold of commercially available UV-grade fused silica. NIF is able to maintain such high fluence operation by using an optics recycling loop strategy. Successful operation of the loop relies on a number of technologies specifically developed for NIF. One of the most important is the capability developed by LLNL and their vendors for producing highly damage-resistant optics. Other technologies developed for the optics recycle loop raise the operating point of NIF by keeping damage growth in check. LLNL has demonstrated the capability to sustain UV fused silica optic recycling rates of up to 40 optics per week. The optics are ready for reinstallation after a 3-week trip through a recycle loop where the damage state of each optic is assessed and repaired. The impact of the optics recycle loop has been profound, allowing the experimental program to routinely employ energies and fluences that would otherwise have been unachievable. Without the recycle loop, it is likely that the NIF fluence would need to be kept below the UV threshold for damage growth, ~4 J/cm
2
, thus keeping the energy delivered to the target significantly below 1 MJ. With the recycle loop implemented during the National Ignition Campaign, NIF can routinely deliver >1.8 MJ on target, an increase in operational capability of more than 100%. In this review, the enabling technological advances, optical performance, and operational capability implications of the optics recycle loop are discussed.
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Indirect-drive hohlraum experiments at the National Ignition Facility have demonstrated symmetric capsule implosions at unprecedented laser drive energies of 0.7 megajoule. One hundred and ninety-two ...simultaneously fired laser beams heat ignition-emulate hohlraums to radiation temperatures of 3.3 million kelvin, compressing 1.8-millimeter-diameter capsules by the soft x-rays produced by the hohlraum. Self-generated plasma optics gratings on either end of the hohlraum tune the laser power distribution in the hohlraum, which produces a symmetric x-ray drive as inferred from the shape of the capsule self-emission. These experiments indicate that the conditions are suitable for compressing deuterium-tritium-filled capsules, with the goal of achieving burning fusion plasmas and energy gain in the laboratory.
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Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) ...flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is difficult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to efficiently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named DeepFlood, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with ≈11 cm root mean square error.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
ObjectiveTo report the oncological outcome and long‐term complications of radical surgery by robot‐assisted laparoscopy in early stage cervical cancer.DesignObservational cohort study.SettingTertiary ...referral centre.PopulationAbout 100 cervical cancer patients treated consecutively with robot‐assisted radical surgery between 2008 and 2013.MethodsTwo gynaecological oncologists specialised in minimally invasive surgery performed all surgeries on a three/four‐armed robotic system. Procedures consisted of pelvic lymph node dissection combined with a radical hysterectomy, radical vaginal trachelectomy or parametrectomy.Main outcome measuresRecurrence, survival and long‐term complication rates.Results104 robot‐assisted laparoscopies were performed in 100 patients (stage IA1–IIB), with a median follow‐up of 29.5 months (range 2.5–67.1 months). Thirteen cases were diagnosed with a loco‐regional (8%), distant (4%) or combined (1%) recurrence at a median of 14.4 months (range 2.9–34.8 months). All mortality (7%) was cervical cancer‐related and due to recurrent disease. Four recurrences receive palliative care and two are in complete remission. The overall 5‐year progression‐free and disease‐specific survival rates are 81.4 and 88.7%, respectively. Frequent complications were lymphoedema (26%), lower urinary tract symptoms (19%), urinary tract infection (17%) and sexual disorders (9%). Five patients had a vaginal cuff dehiscence. No complication‐related mortality occurred.ConclusionThe recurrence, survival and long‐term complication rates of robot‐assisted radical surgery for early stage cervical cancer in this cohort are reassuring concerning its continued clinical use.
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7.
Description of the NIF Laser Spaeth, M. L.; Manes, K. R.; Kalantar, D. H. ...
Fusion science and technology,
02/2016, Volume:
69, Issue:
1
Journal Article
Peer reviewed
Open access
The possibility of imploding small capsules to produce mini-fusion explosions was explored soon after the first thermonuclear explosions in the early 1950s. Various technologies have been pursued to ...achieve the focused power and energy required for laboratory-scale fusion. Each technology has its own challenges. For example, electron and ion beams can deliver the large amounts of energy but must contend with Coulomb repulsion forces that make focusing these beams a daunting challenge. The demonstration of the first laser in 1960 provided a new option. Energy from laser beams can be focused and deposited within a small volume; the challenge became whether a practical laser system can be constructed that delivers the power and energy required while meeting all other demands for achieving a high-density, symmetric implosion. The National Ignition Facility (NIF) is the laser designed and built to meet the challenges for study of high-energy-density physics and inertial confinement fusion (ICF) implosions. This paper describes the architecture, systems, and subsystems of NIF. It describes how they partner with each other to meet these new, complex demands and describes how laser science and technology were woven together to bring NIF into reality.
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The National Ignition Facility (NIF) laser with its 192 independent laser beams is not only the world's largest laser but also the largest optical system ever built. With its 192 independent laser ...beams, the NIF requires a total of 7648 large-aperture (meter-sized) optics. One of the many challenges in designing and building NIF has been to carry out the research and development on optical materials, optics design, and optics manufacturing and metrology technologies needed to achieve NIF's high output energies and precision beam quality. This paper describes the multiyear, multisupplier development effort that was undertaken to develop the advanced optical materials, coatings, fabrication technologies, and associated process improvements necessary to manufacture the wide range of NIF optics. The optics include neodymium-doped phosphate glass laser amplifiers; fused-silica lenses, windows, and phase plates; mirrors and polarizers with multilayer, high-reflectivity dielectric coatings deposited on BK7 substrates; and potassium di-hydrogen phosphate crystal optics for fast optical switches, frequency conversion, and polarization rotation. Also included is a discussion of optical specifications and custom metrology and quality-assurance tools designed, built, and fielded at supplier sites to verify compliance with the stringent NIF specifications. In addition, a brief description of the ongoing program to improve the operational lifetime (i.e., damage resistance) of optics exposed to high fluence in the 351-nm (3ω) is provided.
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Background
While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work ...a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants.
Results
The amino acid descriptor sets compared here show similar performance (<0.1 log units RMSE difference and <0.1 difference in MCC), while errors for individual proteins were in some cases found to be larger than those resulting from descriptor set differences ( > 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last.
Conclusions
While amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that choosing an appropriate descriptor set is of fundamental for bioactivity modeling, both from the ligand- as well as the protein side.
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