Evolving better RNAfold structure prediction

Grow and graft genetic programming (GGGP) evolves more than 50000 parameters in a state-of-the-art C program to make functional source code changes which give more accurate predictions of how RNA molecules fold up. Genetic improvement updates 29% of the dynamic programming free energy model parameters. In most cases (50.3%) GI gives better results on 4655 known secondary structures from RNA_STRAND (29.0% are worse and 20.7% are unchanged). Indeed it also does better than parameters recommended by Andronescu, M., et al.: Bioinformatics 23(13) (2007) i19–i28

Evaluation of surface nuclear magnetic resonance-estimated subsurface water content

The technique of nuclear magnetic resonance (NMR) has found widespread use in geophysical applications for determining rock properties (e.g. porosity and permeability) and state variables (e.g. water content) or to distinguish between oil and water. NMR measurements are most commonly made in the laboratory and in boreholes. The technique of surface NMR (or magnetic resonance sounding (MRS)) also takes advantage of the NMR phenomenon, but by measuring subsurface rock properties from the surface using large coils of some tens of meters and reaching depths as much as 150 m. We give here a brief review of the current state of the art of forward modeling and inversion techniques. In laboratory NMR a calibration is used to convert measured signal amplitudes into water content. Surface NMR-measured amplitudes cannot be converted by a simple calibration. The water content is derived by comparing a measured amplitude with an amplitude calculated for a given subsurface water content model as input for a forward modeling that must account for all relevant physics. A convenient option to check whether the measured signals are reliable or the forward modeling accounts for all effects is to make measurements in a well-defined environment. Therefore, measurements on top of a frozen lake were made with the latest-generation surface NMR instruments. We found the measured amplitudes to be in agreement with the calculated amplitudes for a model of 100 % water content. Assuming then both the forward modeling and the measurement to be correct, the uncertainty of the model is calculated with only a few per cent based on the measurement uncertainty

Improving the accuracy of 1D SNMR surveys using the multi-central-loop configuration

Temeljna svrha i cilj ovoga rada bilo je ispitati koliko su potrošači skloni dijeljenju svojih turističkih iskustva s drugima te putem kojih medija. Osim navedenog, drugi cilj provedenog istraživanja bilo je utvrditi koliko su potrošačima važna iskustva i komentari drugih posjetitelja u procesu donošenja odluke o kupnji. Istraživanje je provedeno metodom ispitivanja, a kao instrument korišten je anketni upitnik sastavljen od 22 pitanja. Utvrđivanjem problema istraživanja, postavljene su tri hipoteze. Od tri hipoteze, u potpunosti je dokazana samo prva koja pretpostavlja da su potrošačima tuđa iskustva i komentari od velike važnosti kod planiranja i odabira putovanja. Druga hipoteza je djelomično potvrđena, tj. potvrđeno je da su potrošači skloni dijeliti svoja iskustva s drugima u situaciji kada su jako zadovoljni dok s druge strane nije potvrđeno kako su potrošači skloni dijeliti svoja iskustva u situaciji kada su nezadovoljni uslugom ili proizvodom. Na kraju, potvrđena je i treća hipoteza koja pretpostavlja kako su potrošači skloni dijeljenju vlastitog turističkog iskustva putem više društvenih medija, iako je utvrđeno kako najveći broj ispitanika ne dijeli svoja turistička iskustva. Istraživano je i mišljenje ispitanika o turističkoj destinaciji iz snova, a iznenađujuće, najveći broj ispitanika je navelo hrvatske destinacije kao svoje destinacije iz snova kao i one koje su im dosada pružile najnezaboravnije turističko iskustvo. Potrebno je provesti detaljnija istraživanja kako bi se detaljnije istražilo novije društvene medije koji su dostupni potrošačima za dijeljenje svog iskustva

Improving the accuracy of 1D SNMR surveys using the multi-central-loop configuration

A multi-central loop configuration has been studied through forward and inverse modelling of synthetics and real data. This set-up takes advantage of the multichannel features of the NMR device and consists of using several (2 to 3) additional receiver loops displayed concentrically with the main transmitter/receiver loop, which all record the NMR signal simultaneously within a single acquisition. If the loop diameters are chosen appropriately, the kernel sensitivity distributions for each receiver loop can show complementary features. Inverting simultaneously the data sets obtained through each different receiver loop can then enhance the accuracy of the final model. To do so, a 1D QT inversion scheme in the frequency domain dedicated to the inversion of multiple data sets is being used. One challenging feature is to adapt the regularization of the inverse process so as to handle correctly the noise originating from different data sets. The efficiency of this multi-central loop acquisition set-up and procedure is being assessed through the forward and inverse modelling of several scenarios implying varying aquifer characteristics. Finally a field case is being presented that was conducted on a low noise level site located in Germany, where conditions were favourable to the implementation and testing of circular multi-central loop configurations.We also introduce a new method for determining NMR parameters, named the prediction-focused-approach (PFA), that is based on statistical analysis of a large number of simple models. We observe, using synthetic examples, that the effciency of the method benefits from the use of the multi-central-loop configurations

Darwinian Data Structure Selection

Data structure selection and tuning is laborious but can vastly improve an application's performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for \emph{every} project in $5$ Java projects from DaCapo benchmark, $8$ popular projects and $30$ uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves \emph{all} measures for $86\%$ ($37/43$) of the projects. The median improvement across the best solutions is $4.8\%$, $10.1\%$, $5.1\%$ for runtime, memory and CPU usage. These aggregate results understate ARTEMIS's potential impact. Some of the benchmarks it improves are libraries or utility functions. Two examples are gson, a ubiquitous Java serialization framework, and xalan, Apache's XML transformation tool. ARTEMIS improves gson by $16.5$\%, $1\%$ and $2.2\%$ for memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by $23.5$\%. \emph{Every} client of these projects will benefit from these performance improvements.Comment: 11 page

The radical cation of bacteriochlorophyll b. A liquid-phase endor and triple resonance study

The previous termradical cationnext term of bacterioehlorophyll b (BChl b) is investigated by ENDOR and TRIPLE resonance in liquid solution. The experimental hyperfine coupling constants, ten proton and three nitrogen couplings, are compared with the predictions from advanced molecular-orbital calculations (RHF INDO/SP). The detailed picture obtained of the spin density distribution is a prerequisite for the investigation of the primary electron donor previous termradical cationnext term in BChl b containing photosynthetic bacteria

A Survey of Genetic Improvement Search Spaces

Genetic Improvement (GI) uses automated search to improve existing software. Most GI work has focused on empirical studies that successfully apply GI to improve software's running time, fix bugs, add new features, etc. There has been little research into why GI has been so successful. For example, genetic programming has been the most commonly applied search algorithm in GI. Is genetic programming the best choice for GI? Initial attempts to answer this question have explored GI's mutation search space. This paper summarises the work published on this question to date

Gin: Genetic Improvement Research Made Easy

Genetic improvement (GI) is a young field of research on the cusp of transforming software development. GI uses search to improve existing software. Researchers have already shown that GI can improve human-written code, ranging from program repair to optimising run-time, from reducing energy-consumption to the transplantation of new functionality. Much remains to be done. The cost of re-implementing GI to investigate new approaches is hindering progress. Therefore, we present Gin, an extensible and modifiable toolbox for GI experimentation, with a novel combination of features. Instantiated in Java and targeting the Java ecosystem, Gin automatically transforms, builds, and tests Java projects. Out of the box, Gin supports automated test-generation and source code profiling. We show, through examples and a case study, how Gin facilitates experimentation and will speed innovation in GI

Genetic Improvement @ ICSE 2020

Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the International Conference on Software En- gineering on Friday 3rd July 2020). Topics included industry take up, human factors, explainabiloity (explainability, jus- tifyability, exploitability) and GI benchmarks. We also con- trast various recent online approaches (e.g. SBST 2020) to holding virtual computer science conferences and workshops via the WWW on the Internet without face to face interac- tion. Finally we speculate on how the Coronavirus Covid-19 Pandemic will a ect research next year and into the future

The Spatial Distribution of LGR5+ Cells Correlates With Gastric Cancer Progression

In this study we tested the prevalence, histoanatomical distribution and tumour biological significance of the Wnt target protein and cancer stem cell marker LGR5 in tumours of the human gastrointestinal tract. Differential expression of LGR5 was studied on transcriptional (real-time polymerase chain reaction) and translational level (immunohistochemistry) in malignant and corresponding non-malignant tissues of 127 patients comprising six different primary tumour sites, i.e. oesophagus, stomach, liver, pancreas, colon and rectum. The clinico-pathological significance of LGR5 expression was studied in 100 patients with gastric carcinoma (GC). Non-neoplastic tissue usually harboured only very few scattered LGR5+ cells. The corresponding carcinomas of the oesophagus, stomach, liver, pancreas, colon and rectum showed significantly more LGR5+ cells as well as significantly higher levels of LGR5-mRNA compared with the corresponding non-neoplastic tissue. Double staining experiments revealed a coexpression of LGR5 with the putative stem cell markers CD44, Musashi-1 and ADAM17. Next we tested the hypothesis that the sequential changes of gastric carcinogenesis, i.e. chronic atrophic gastritis, intestinal metaplasia and invasive carcinoma, are associated with a reallocation of the LGR5+ cells. Interestingly, the spatial distribution of LGR5 changed: in non-neoplastic stomach mucosa, LGR5+ cells were found predominantly in the mucous neck region; in intestinal metaplasia LGR5+ cells were localized at the crypt base, and in GC LGR5+ cells were present at the luminal surface, the tumour centre and the invasion front. The expression of LGR5 in the tumour centre and invasion front of GC correlated significantly with the local tumour growth (T-category) and the nodal spread (N-category). Furthermore, patients with LGR5+ GCs had a shorter median survival (28.0±8.6 months) than patients with LGR5− GCs (54.5±6.3 months). Our results show that LGR5 is differentially expressed in gastrointestinal cancers and that the spatial histoanatomical distribution of LGR5+ cells has to be considered when their tumour biological significance is sought