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The growing burden of wounds is placing pressure on clinicians and healthcare providers globally. This study aimed to determine the impact of using a hydrocellular polyurethane foam dressing (HPFD) on weekly dressing changes in mixed aetiology wounds. A systematic literature review was performed to identify studies reporting HPFD use versus other/previous dressings via PubMed, EMBASE and the Cochrane Library. A total of four studies were found reporting the use of HPFD in comparison to other dressings (386 patients), with 3 of these studies specifically providing data for HPFD use versus other (non‐HPFD) foam dressings (136 patients). Meta‐analysis revealed mean differences of −1.66 [95% CI −1.87;−1.48] and −1.23 [95% CI −1.65;−0.83] dressing changes per week in favour of HPFD versus other dressings and other foam dressings, respectively. A cost calculator model assessed the economic impact of this reduction of dressing change frequency from US and UK payer perspectives and found a 41% and 36% relative reduction in dressing change‐related costs in hospital/clinic and community settings for both perspectives. These findings were stable under sensitivity analyses, which identified clinical time as being the predominant driver of costs in all settings. These findings highlight the importance of appropriate evidence‐based dressing selection in enabling the extension of dressing wear times. Key Points The increasing burden of wound management weighs heavily on already stretched clinical and financial resources within healthcare systems. Use of specific evidence‐based dressings led to a reduction in the number of mean weekly dressing changes in a variety of healthcare settings and wounds when compared to previously used dressings. A health economic model estimated that the reduction in dressing changes resulted in substantial cost savings, primarily through a reduction in nursing time. Evidence‐based dressing selection can drive meaningful efficiencies in wound care delivery by releasing clinical resource and reducing costs.

The [3,3]-sigmatropic rearrangement of N-allylhydrazones is a potentially powerful, yet under-explored reaction that forges a new carbon–carbon bond while generating a labile monoalkyldiazine intermediate. Subsequent loss of dinitrogen affords a product containing no trace of the original hydrazone functionality. Our efforts to render the [3,3]-sigmatropic rearrangement of N-allylhydrazones a reliable traceless fragment coupling reaction have led to the discovery and development of several novel N-allylhydrazone-based transformations. Early investigations unearthed a copper(II) chloride-promoted tandem carbon–carbon and carbon–chlorine bond forming [3,3]-sigmatropic rearrangement of N-allylhydrazones. This discovery led to the development of a reliable method for the synthesis of homoallylic chlorides. Extension of this reactivity to include tandem carbon–carbon and carbon–bromine bond formation allowed for the development of a stereoselective, hydrazone-based synthesis of dienes. In line with our initial goal, efforts toward an N-allyhydrazone-based traceless fragment coupling led to the discovery and development of a triflimide-catalyzed [3,3]-sigmatropic rearrangement of N-allylhydrazones. Finally, further studies in the area of hydrazone-based traceless fragment coupling reactions have led to the development of a traceless Petasis–Mannich reaction for the synthesis of allenes.

The Dow Jones Industrial Average, which started with 12 stocks in 1897, is the oldest continuous price index of the U.S. stock market. By the turn of the century, the DJIA included American Cotton Oil, Chicago Gas, Distilling and Cattle Feeding, and U.S. Leather. In 1928, the index was expanded to its present size of 30 stocks. Fifteen of these stocks still survive in the index, although in many cases the companies have changed their names. The original 30 stocks were heavily concentrated in automotive issues like General Motors, Chrysler, Mack Trucks and Nash Motors. A 1953 Financial Analysts Journal article by Butler and Decker recommended that the index be broadened to include three important industries as yet unrepresented -- paper, office equipment and pharmaceuticals. Three years later, International Paper was added. Another quarter of a century elapsed before IBM and Merck joined the list. One of the problems with the current index is its sensitivity to stock splits. The original practice of entering a company at twice its current price after a two for one stock split (or three times its price following a three for one split, etc.) was dropped in 1928. Now a split reduces a stock's weighting in the index while the divisor changes by the precise amount necessary to preserve the value of the average. The authors recommend that the original practice be resumed. The authors also feel that the following substitutions would make the index more representative of U.S. industry in the 1980s: Dow Chemical for Allied Chemical, Philip Morris for American Brands, Caterpillar Tractor for International Harvester, Standard Oil of Indiana for Texaco, Boeing for Westinghouse Electric, K Mart for Woolworth.

The last three decades have witnessed a fairly well defined 10-year cycle in the aerospace industry, with the industry doing well in the middle of each decade. If the oil crisis has caused a two- or three-year delay in the current cycle, 1977 and the years beyond could be good ones for airframe manufacturers.

Because the development of new commercial airplanes can cost more than a billion dollars, the time between program inception and payout is long. In the interim, the manufacturer is exposed to a number of risks, including technological obsolescence, supplier problems and crashes that undermine public confidence in the airplane. Nevertheless, with first generation commercial jets becoming obsolete, the world market for commercial aircraft should be very large over the decade ahead.

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

\"We're certainly screaming up, making new highs,\" said Leonard Kaplan, president of Prospector Asset Management. \"There is fund buying all of the way up. Gold was up 30% last year. People are riding the winner.\" \"Most of the metals are higher,\" Mr. [Andrew Montano] at Scotia Mocatta said. \"Copper is quite a bit higher. Oil is also recovering yesterday's slide. It seems to be a bit of a commodity day.\"

\"We're certainly screaming up, making new highs,\" said Leonard Kaplan, president of Prospector Asset Management. \"There is fund buying all of the way up. Gold was up 30% last year. People are riding the winner.\" \"Most of the metals are higher,\" Mr. [Andrew Montano] at Scotia Mocatta said. \"Copper is quite a bit higher. Oil is also recovering yesterday's slide. It seems to be a bit of a commodity day.\"

Matrix factorization models are the current dominant approach for resolving meaningful data-driven features in neuroimaging data. Among them, independent component analysis (ICA) is arguably the most widely used for identifying functional networks, and its success has led to a number of versatile extensions to group and multimodal data. However there are indications that ICA may have reached a limit in flexibility and representational capacity, as the majority of such extensions are case-driven, custom-made solutions that are still contained within the class of mixture models. In this work, we seek out a principled and naturally extensible approach and consider a probabilistic model known as a restricted Boltzmann machine (RBM). An RBM separates linear factors from functional brain imaging data by fitting a probability distribution model to the data. Importantly, the solution can be used as a building block for more complex (deep) models, making it naturally suitable for hierarchical and multimodal extensions that are not easily captured when using linear factorizations alone. We investigate the capability of RBMs to identify intrinsic networks and compare its performance to that of well-known linear mixture models, in particular ICA. Using synthetic and real task fMRI data, we show that RBMs can be used to identify networks and their temporal activations with accuracy that is equal or greater than that of factorization models. The demonstrated effectiveness of RBMs supports its use as a building block for deeper models, a significant prospect for future neuroimaging research. •A naturally extensible novel method for separating intrinsic networks from fMRI data•Effective performance of the method in comparison to state of the art in simulations•Effective intrinsic networks separation in task-related fMRI data•Enhanced time course and functional connectivity estimates•Overall competitive approach naturally extensible to group and multimodal settings