Evaluation of thunderstorm predictors in Finland from ECMWF reanalyses and lightning location data

While numerical weather forecasts have improved dramatically in recent decades, forecasting severe weather events remains a great challenge due to models being unable to resolve convection explicitly. Forecasters commonly utilize large-scale convective parameters derived from atmospheric soundings to assess whether the atmosphere has the potential to develop convective storms. These parameters are able to describe the environments in which thunderstorms occur but relate to actual thunderstorm events only probabilistically. Roine (2001) used atmospheric soundings and thunderstorm observations to assess which from a variety of stability indices were most successful in predicting thunderstorms in Finland, and found that Surface Lifted Index, CAPE and the Showalter index were most skillful based on the data set in question. This study aims to extend the assessment of thunderstorm predictors to atmospheric reanalyses, by utilising model pseudo-soundings. Reanalyses such as ERA-Interim use sophisticated data assimilation schemes to reconstruct past atmospheric conditions from historical observational data. In addition to a large sample size, this approach enables examining the use of other large-scale model parameters, which are hypothesized to be associated with convective initiation, as supplemental forecast parameters. Using lightning location data and ERA-Interim reanalysis fields for Finnish summers between 2002 and 2013, it is found that the Lifted Index (LI) based on the most unstable parcel in the lowest 300 hPa has the highest forecast skill among traditional stability indices. By combining this index with the dew point depression at 700 hPa and low-level vertical shear, its performance can be further slightly increased. Moreover, vertically integrated mass flux convergence between the surface and 500 hPa calculated from the ERA-I convergence seems to have high association with thunderstorm occurrence when used as a supplementary parameter. Finally, artificial neural networks (ANN) were developed for predicting thunderstorm occurrence, and their forecast skill compared to that of stability indices. The best ANN found, utilizing 11 parameters as input, clearly outperformed the best stability indices in a skill score test; achieving a True Skill Score of 0.69 compared to 0.61 with the most unstable Lifted Index. The results suggest that ANNs, due to their inherent nonlinearity, represent a promising tool for forecasting of deep, moist convection

High-yield production of biologically active recombinant protein in shake flask culture by combination of enzyme-based glucose delivery and increased oxygen transfer

This report describes the combined use of an enzyme-based glucose release system (EnBase®) and high-aeration shake flask (Ultra Yield Flask™). The benefit of this combination is demonstrated by over 100-fold improvement in the active yield of recombinant alcohol dehydrogenase expressed in E. coli. Compared to Terrific Broth and ZYM-5052 autoinduction medium, the EnBase system improved yield mainly through increased productivity per cell. Four-fold increase in oxygen transfer by the Ultra Yield Flask contributed to higher cell density with EnBase but not with the other tested media, and consequently the product yield per ml of EnBase culture was further improved

Twelve times faster yet accurate: a new state‐of‐the‐art in radiation schemes via performance and spectral optimization

Radiation schemes are critical components of Earth system models that need to be both efficient and accurate. Despite the use of approximations such as 1D radiative transfer, radiation can account for a large share of the runtime of expensive climate simulations. Here we seek a new state‐of‐the‐art in speed and accuracy by combining code optimization with improved algorithms. To fully benefit from new spectrally reduced gas optics schemes, we restructure code to avoid short vectorized loops where possible by collapsing the spectral and vertical dimensions. Our main focus is the ecRad radiation scheme, where this requires batching of adjacent cloudy layers, trading some simplicity for improved vectorization and instruction‐level parallelism. When combined with common optimization techniques for serial code and porting widely used two‐stream kernels fully to single precision, we find that ecRad with the TripleClouds solver becomes 12 times faster than the operational radiation scheme in ECMWF's Integrated Forecast System (IFS) cycle 47r3, which uses a less accurate gas optics model (RRMTG) and a more noisy solver (McICA). After applying the spectral reduction and extensive optimizations to the more sophisticated SPARTACUS solver, we find that it’s 2.5 times faster than IFS cy47r3 radiation, making cloud 3D radiative effects affordable to compute in large‐scale models. The code optimization itself gave a threefold speedup for both solvers. While SPARTACUS is still under development, preliminary experiments show slightly improved medium‐range forecasts of 2‐m temperature in the tropics, and in year‐long coupled atmosphere‐ocean simulations the 3D effects warm the surface substantially

A novel fed-batch based cultivation method provides high cell-density and improves yield of soluble recombinant proteins in shaken cultures

<p>Abstract</p> <p>Background</p> <p>Cultivations for recombinant protein production in shake flasks should provide high cell densities, high protein productivity per cell and good protein quality. The methods described in laboratory handbooks often fail to reach these goals due to oxygen depletion, lack of pH control and the necessity to use low induction cell densities. In this article we describe the impact of a novel enzymatically controlled fed-batch cultivation technology on recombinant protein production in <it>Escherichia coli </it>in simple shaken cultures.</p> <p>Results</p> <p>The enzymatic glucose release system together with a well-balanced combination of mineral salts and complex medium additives provided high cell densities, high protein yields and a considerably improved proportion of soluble proteins in harvested cells. The cultivation method consists of three steps: 1) controlled growth by glucose-limited fed-batch to OD₆₀₀~10, 2) addition of growth boosters together with an inducer providing efficient protein synthesis within a 3 to 6 hours period, and 3) a slow growth period (16 to 21 hours) during which the recombinant protein is slowly synthesized and folded. Cell densities corresponding to 10 to 15 g l^-1cell dry weight could be achieved with the developed technique. In comparison to standard cultures in LB, Terrific Broth and mineral salt medium, we typically achieved over 10-fold higher volumetric yields of soluble recombinant proteins.</p> <p>Conclusions</p> <p>We have demonstrated that by applying the novel EnBase^®Flo cultivation system in shaken cultures high cell densities can be obtained without impairing the productivity per cell. Especially the yield of soluble (correctly folded) proteins was significantly improved in comparison to commonly used LB, Terrific Broth or mineral salt media. This improvement is thought to result from a well controlled physiological state during the whole process. The higher volumetric yields enable the use of lower culture volumes and can thus significantly reduce the amount of time and effort needed for downstream processing or process optimization. We claim that the new cultivation system is widely applicable and, as it is very simple to apply, could widely replace standard shake flask approaches.</p

Accelerating radiation computations for dynamical models with targeted machine learning and code optimization

Atmospheric radiation is the main driver of weather and climate, yet due to a complicated absorption spectrum, the precise treatment of radiative transfer in numerical weather and climate models is computationally unfeasible. Radiation parameterizations need to maximize computational efficiency as well as accuracy, and for predicting the future climate many greenhouse gases need to be included. In this work, neural networks (NNs) were developed to replace the gas optics computations in a modern radiation scheme (RTE+RRTMGP) by using carefully constructed models and training data. The NNs, implemented in Fortran and utilizing BLAS for batched inference, are faster by a factor of 1–6, depending on the software and hardware platforms. We combined the accelerated gas optics with a refactored radiative transfer solver, resulting in clear‐sky longwave (shortwave) fluxes being 3.5 (1.8) faster to compute on an Intel platform. The accuracy, evaluated with benchmark line‐by‐line computations across a large range of atmospheric conditions, is very similar to the original scheme with errors in heating rates and top‐of‐atmosphere radiative forcings typically below 0.1 K day−1 and 0.5 W m−2, respectively. These results show that targeted machine learning, code restructuring techniques, and the use of numerical libraries can yield material gains in efficiency while retaining accuracy

Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and Code Optimization

Atmospheric radiation is the main driver of weather and climate, yet due to a complicated absorption spectrum, the precise treatment of radiative transfer in numerical weather and climate models is computationally unfeasible. Radiation parameterizations need to maximize computational efficiency as well as accuracy, and for predicting the future climate many greenhouse gases need to be included. In this work, neural networks (NNs) were developed to replace the gas optics computations in a modern radiation scheme (RTE+RRTMGP) by using carefully constructed models and training data. The NNs, implemented in Fortran and utilizing BLAS for batched inference, are faster by a factor of 1&ndash;6, depending on the software and hardware platforms. We combined the accelerated gas optics with a refactored radiative transfer solver, resulting in clear-sky longwave (shortwave) fluxes being 3.5 (1.8) faster to compute on an Intel platform. The accuracy, evaluated with benchmark line-by-line computations across a large range of atmospheric conditions, is very similar to the original scheme with errors in heating rates and top-of-atmosphere radiative forcings typically below 0.1&nbsp;K&nbsp;day&minus;1 and 0.5&nbsp;W&nbsp;m&minus;2, respectively. These results show that targeted machine learning, code restructuring techniques, and the use of numerical libraries can yield material gains in efficiency while retaining accuracy. </div

Pipeline for Large-Scale Microdroplet Bisulfite PCR-Based Sequencing Allows the Tracking of Hepitype Evolution in Tumors

Cytosine methylation provides an epigenetic level of cellular plasticity that is important for development, differentiation and cancerogenesis. We adopted microdroplet PCR to bisulfite treated target DNA in combination with second generation sequencing to simultaneously assess DNA sequence and methylation. We show measurement of methylation status in a wide range of target sequences (total 34 kb) with an average coverage of 95% (median 100%) and good correlation to the opposite strand (rho = 0.96) and to pyrosequencing (rho = 0.87). Data from lymphoma and colorectal cancer samples for SNRPN (imprinted gene), FGF6 (demethylated in the cancer samples) and HS3ST2 (methylated in the cancer samples) serve as a proof of principle showing the integration of SNP data and phased DNA-methylation information into “hepitypes” and thus the analysis of DNA methylation phylogeny in the somatic evolution of cancer