A comprehensive review of deep learning applications in hydrology and water resources.

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.

Approximate maximum likelihood estimation for population genetic inference.

In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. However, these methods such as Approximate Bayesian Computation (ABC) can be inefficient in high-dimensional problems. This led to the development of more sophisticated iterative estimation methods like particle filters. Here, we propose an alternative approach that is based on stochastic approximation. By moving along a simulated gradient or ascent direction, the algorithm produces a sequence of estimates that eventually converges to the maximum likelihood estimate, given a set of observed summary statistics. This strategy does not sample much from low-likelihood regions of the parameter space, and is fast, even when many summary statistics are involved. We put considerable efforts into providing tuning guidelines that improve the robustness and lead to good performance on problems with high-dimensional summary statistics and a low signal-to-noise ratio. We then investigate the performance of our resulting approach and study its properties in simulations. Finally, we re-estimate parameters describing the demographic history of Bornean and Sumatran orang-utans.

Thinking too positive? Revisiting current methods of population genetic selection inference.

In the age of next-generation sequencing, the availability of increasing amounts and improved quality of data at decreasing cost ought to allow for a better understanding of how natural selection is shaping the genome than ever before. However, alternative forces, such as demography and background selection (BGS), obscure the footprints of positive selection that we would like to identify. In this review, we illustrate recent developments in this area, and outline a roadmap for improved selection inference. We argue (i) that the development and obligatory use of advanced simulation tools is necessary for improved identification of selected loci, (ii) that genomic information from multiple time points will enhance the power of inference, and (iii) that results from experimental evolution should be utilized to better inform population genomic studies.

Influenza virus drug resistance: a time-sampled population genetics perspective.

The challenge of distinguishing genetic drift from selection remains a central focus of population genetics. Time-sampled data may provide a powerful tool for distinguishing these processes, and we here propose approximate Bayesian, maximum likelihood, and analytical methods for the inference of demography and selection from time course data. Utilizing these novel statistical and computational tools, we evaluate whole-genome datasets of an influenza A H1N1 strain in the presence and absence of oseltamivir (an inhibitor of neuraminidase) collected at thirteen time points. Results reveal a striking consistency amongst the three estimation procedures developed, showing strongly increased selection pressure in the presence of drug treatment. Importantly, these approaches re-identify the known oseltamivir resistance site, successfully validating the approaches used. Enticingly, a number of previously unknown variants have also been identified as being positively selected. Results are interpreted in the light of Fisher's Geometric Model, allowing for a quantification of the increased distance to optimum exerted by the presence of drug, and theoretical predictions regarding the distribution of beneficial fitness effects of contending mutations are empirically tested. Further, given the fit to expectations of the Geometric Model, results suggest the ability to predict certain aspects of viral evolution in response to changing host environments and novel selective pressures.

The consequences of not accounting for background selection in demographic inference.

Recently, there has been increased awareness of the role of background selection (BGS) in both data analysis and modelling advances. However, BGS is still difficult to take into account because of tractability issues with simulations and difficulty with nonequilibrium demographic models. Often, simple rescaling adjustments of effective population size are used. However, there has been neither a proper characterization of how BGS could bias or shift inference when not properly taken into account, nor a thorough analysis of whether rescaling is a sufficient solution. Here, we carry out extensive simulations with BGS to determine biases and behaviour of demographic inference using an approximate Bayesian approach. We find that results can be positively misleading with significant bias, and describe the parameter space in which BGS models replicate observed neutral nonequilibrium expectations.

Inferring the age of a fixed beneficial allele.

Estimating the age and strength of beneficial alleles is central to understanding how adaptation proceeds in response to changing environmental conditions. Several haplotype-based estimators exist for inferring the age of segregating beneficial mutations. Here, we develop an approximate Bayesian-based approach that rather estimates these parameters for fixed beneficial mutations in single populations. We integrate a range of existing diversity, site frequency spectrum, haplotype- and linkage disequilibrium-based summary statistics. We show that for strong selective sweeps on de novo mutations the method can estimate allele age and selection strength even in nonequilibrium demographic scenarios. We extend our approach to models of selection on standing variation, and co-infer the frequency at which selection began to act upon the mutation. Finally, we apply our method to estimate the age and selection strength of a previously identified mutation underpinning cryptic colour adaptation in a wild deer mouse population, and compare our findings with previously published estimates as well as with geological data pertaining to the presumed shift in selective pressure.

MSMS: a coalescent simulation program including recombination, demographic structure and selection at a single locus.

MOTIVATION: We have implemented a coalescent simulation program for a structured population with selection at a single diploid locus. The program includes the functionality of the simulator ms to model population structure and demography, but adds a model for deme- and time-dependent selection using forward simulations. The program can be used, e.g. to study hard and soft selective sweeps in structured populations or the genetic footprint of local adaptation. The implementation is designed to be easily extendable and widely deployable. The interface and output format are compatible with ms. Performance is comparable even with selection included. AVAILABILITY: The program is freely available from http://www.mabs.at/ewing/msms/ along with manuals and examples. The source is freely available under a GPL type license.

Rooted triple consensus and anomalous gene trees.

BACKGROUND: Anomalous gene trees (AGTs) are gene trees with a topology different from a species tree that are more probable to observe than congruent gene trees. In this paper we propose a rooted triple approach to finding the correct species tree in the presence of AGTs. RESULTS: Based on simulated data we show that our method outperforms the extended majority rule consensus strategy, while still resolving the species tree. Applying both methods to a metazoan data set of 216 genes, we tested whether AGTs substantially interfere with the reconstruction of the metazoan phylogeny. CONCLUSION: Evidence of AGTs was not found in this data set, suggesting that erroneously reconstructed gene trees are the most significant challenge in the reconstruction of phylogenetic relationships among species with current data. The new method does however rule out the erroneous reconstruction of deep or poorly resolved splits in the presence of lineage sorting.

Synthesis of 2',3'-dideoxynucleoside 5'-alpha-P-borano-beta,gamma-(difluoromethylene)triphosphates and their inhibition of HIV-1 reverse transcriptase.

The triphosphates of antiviral 2',3'-dideoxynucleosides (ddNs) are the active chemical species that inhibit viral DNA synthesis. The inhibition involves incorporation of ddNMP into DNA and subsequent chain termination. A conceivable strategy for antiviral drugs is to employ nucleoside 5'-triphosphate mimics that can entirely bypass cellular phosphorylation. AZT 5'-alpha-R(P)-borano-beta,gamma-(difluoromethylene)triphosphate (5'-alphaB-betagammaCF(2)TP) has been identified as a potent inhibitor of HIV-1 reverse transcriptase (HIV-1 RT). This work was aimed at confirming that 5'-alphaB-betagammaCF(2)TP is a useful generic triphosphate moiety and can render antiviral ddNs with potent inhibitory effects on HIV-1 RT. Thus, 10 ddNs were converted to their 5'-alphaB-betagammaCF(2)TPs via a sequence (one-pot) of reactions: formation of an activated phosphite, formation of a cyclic triphosphate, boronation, and hydrolysis. Other synthetic routes were also explored. All ddN 5'-alphaB-betagammaCF(2)TPs tested exhibited essentially the same level of inhibition of HIV-1 RT as the corresponding ddNTPs. A conclusion can be made that 5'-alphaB-betagammaCF(2)TP is a generic and promising triphosphate mimic (P3M) concerning HIV-1 RT inhibition and serum stability. It is anticipated that use of 5'-alphaB-betagammaCF(2)TP as P3M moiety will lead to the discovery of a new class of anti-HIV agents.

PopPlanner: visually constructing demographic models for simulation.

UNLABELLED: Currently there are a number of coalescent simulation programs that support a wide range of features, such as arbitrary demographic models, migration, and sub structure. Defining the model is done typically with either text files or command line switches. Although this has proven to be a powerful method of defining models of high complexity, it is often error prone and difficult to read without familiarity both with command lines and the program in question. A intuitive GUI based population structure program that can both read and write applicable command lines would dramatically simplify the construction, modification, and error checking of such models by a wider user base. RESULTS: PopPlanner is a tool to both construct and inspect complicated demographic models visually with a GUI where the user's primary interaction is through mouse gestures. Because of their popularity, we focus on ms and by extension msms, command line coalescent simulation programs. Our program can be used to find errors with existing command lines, or to build original command lines. Furthermore, the graphical output supports a number of editing and output features including export of publication quality figures.

S-homoadenosyl-L-cysteine and S-homoadenosyl-L-homocysteine. Synthesis and binding studies of hon-hydrolyzed substrate analogues with S-adenosyl-L-homocysteine hydrolase.

Treatment of homoadenosine [9-(5-deoxy-beta-D-ribo-hexofuranosyl)adenine] with thionyl chloride and pyridine in acetonitrile gave 6'-chloro-6'-deoxyhomoadenosine, which underwent nucleophilic displacement with L-cysteine or L-homocysteine to give homologated analogues of S-adenosyl-L-homocysteine. Each amino acid in aqueous sodium hydroxide at 60 degrees C gave excellent conversion from the chloronucleoside, and adsorption on Amberlite XAD-4 resin provided more convenient isolation than prior methods. Weak binding of these non-hydrolyzed analogues to S-adenosyl-L-homocysteine hydrolase was observed.