Quantifying the mutual effects of water trading and systematic water saving in a water-scarce watershed of China.

In this study, a conjunctive water management model based on interval stochastic bi-level programming method (CM-ISBP) is proposed for planning water trading program as well as quantifying mutual effects of water trading and systematic water saving. CM-ISBP incorporates water resources assessment with soil and water assessment tool (SWAT), systematic water-saving simulation combined with water trading, and interval stochastic bi-level programming (ISBP) within a general framework. Systematic water saving involves irrigation water-saving technologies (sprinkler irrigation, micro-irrigation, low-pressure pipe irrigation), enterprise water-saving potential and water-saving subsidy. The CM-ISBP is applied to a real case of a water-scarce watershed (i.e. Dagu River watershed, China). Mutual effects of water trading and water-saving activities are simulated with model establishment and quantified through mechanism analysis. The fate of saved water under the systematic water saving is also revealed. The coexistence of the two systems would increase system benefits by [11.89, 12.19]%, and increase the water use efficiency by [40.04, 40.46]%. Thus mechanism that couples water trading and water saving is optimal and recommended according to system performance.

Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.

PseU-KeMRF: A novel method for identifying RNA pseudouridine sites.

Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of various biological mechanisms of pseudouridine. However, the use of traditional experimental methods faces certain challenges. The development of fast and convenient computational methods is necessary to accurately identify pseudouridine sites from RNA sequence information. To address this, we introduce a novel pseudouridine site prediction model called PseU-KeMRF, which can identify pseudouridine sites in three species, H. sapiens, S. cerevisiae, and M. musculus. Through comprehensive analysis, we selected four RNA coding schemes, including binary feature, position-specific trinucleotide propensity based on single strand (PSTNPss), nucleotide chemical property (NCP) and pseudo k-tuple composition (PseKNC). Then the support vector machine-recursive feature elimination (SVM-RFE) method was used for feature selection and the feature subset was optimized. Finally, the best feature subsets are input into the kernel based on multinomial random forests (KeMRF) classifier for cross-validation and independent testing. As a new classification method, compared with the traditional random forest, KeMRF not only improves the node splitting process of decision tree construction based on multinomial distribution, but also combines the easy to interpret kernel method for prediction, which makes the classification performance better. Our results indicate superior predictive performance of PseU-KeMRF over other existing models. On the three species' training datasets, the testing accuracy of PseU-KeMRF was 0.66%, 3.66%, and 2.76% higher, respectively, than the best available methods. Moreover, PseU-KeMRF's accuracy on independent testing datasets was 15.15% and 11.0% higher, respectively, than the best available methods. The above results can prove that PseU-KeMRF is a highly competitive predictive model that can successfully identify pseudouridine sites in RNA sequences.

Water rights trading planning and its application in water resources management: A water-ecology-food nexus perspective.

Nexus approach provides an effective perspective for implementing synergetic management of water resources. In this study, an interval two-stage chance-constrained water rights trading planning model under water-ecology-food nexus perspective (ITCWR-WEF) is proposed to analyze the interaction between water trading and water-ecology-food (WEF) nexus, which fills in the water resources management gaps from a novel nexus perspective. ITCWR-WEF incorporates hydrological simulation with soil and water assessment tool (SWAT), water rights configuration with interval two-stage chance-constrained programming (ITCP), and multi-criterion analysis with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The developed ITCWR-WEF is applied to a real case of Daguhe watershed, which has characteristics of water scarcity, food producing areas and fragile ecosystem. Initial water rights allocation is addressed before the trading. Mechanisms analysis is designed to reveal mutual effect of water rights trading and WEF nexus. Optimal water management scenario is identified through multi-criterion analysis. Results reveal that the mechanism of water rights trading with WEF nexus under low constraint-violation risk level of water availability and environment capacity is recommended to promote the rational water resources allocation to balance the economic goals, water environment and water supply security, as well as ecological and food water demand guarantees.

RAVAR: a curated repository for rare variant-trait associations.

Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.

Design of polymetallic sulfide NiS2@Co4S3@FeS as bifunctional catalyst for high efficiency seawater splitting.

The shortage of freshwater resources in the world today has limited the development of water splitting, and our eyes have turned to the abundant seawater. The development of relatively low-toxicity and high-efficiency catalysts is the most important area in seawater electrolysis. In this paper, the preparation of NiS2@Co4S3@FeS via a hydrothermal method on nickel foam has been studied for the first time. In the process of vulcanization, Fe will first generate FeS by virtue of its high affinity for vulcanization. Once Fe is vulcanized, the residual sulfur will be used to generate NiS2, while the vulcanization of Co requires a higher sulfur concentration and reaction temperature; thus, Co4S3 will be generated last. NiS2@Co4S3@FeS is confirmed to have excellent hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) catalytic properties in alkaline seawater. Its unique structure allows it to expose more reaction centres, and the synergies between the multiple metals optimize the charge distribution of the material and accelerate the OER and HER kinetics. NiS2@Co4S3@FeS requires overpotentials of only 122 mV and 68 mV for the OER and HER when reaching 10 mA cm-2, which is superior to most catalysts reported to date for seawater electrolysis, and the material displays acceptable stability. In an electrolytic cell composed of both positive and negative electrodes, when the current density is 10 mA cm-2, the NiS2@Co4S3@FeS material displays a low overpotential of only 357 mV for seawater splitting. Density functional theory shows that the FeS electrode has the optimum Gibbs free energy of H to accelerate reaction kinetics, and the synergistic catalysis of the NiS2, Co4S3 and FeS materials promotes the hydrogen production activity of the NiS2@Co4S3@FeS electrode. This work proposes a novel idea for designing environmentally friendly seawater splitting catalysts.

iPseU-TWSVM: Identification of RNA pseudouridine sites based on TWSVM.

Biological sequence analysis is an important basic research work in the field of bioinformatics. With the explosive growth of data, machine learning methods play an increasingly important role in biological sequence analysis. By constructing a classifier for prediction, the input sequence feature vector is predicted and evaluated, and the knowledge of gene structure, function and evolution is obtained from a large amount of sequence information, which lays a foundation for researchers to carry out in-depth research. At present, many machine learning methods have been applied to biological sequence analysis such as RNA gene recognition and protein secondary structure prediction. As a biological sequence, RNA plays an important biological role in the encoding, decoding, regulation and expression of genes. The analysis of RNA data is currently carried out from the aspects of structure and function, including secondary structure prediction, non-coding RNA identification and functional site prediction. Pseudouridine (У) is the most widespread and rich RNA modification and has been discovered in a variety of RNAs. It is highly essential for the study of related functional mechanisms and disease diagnosis to accurately identify У sites in RNA sequences. At present, several computational approaches have been suggested as an alternative to experimental methods to detect У sites, but there is still potential for improvement in their performance. In this study, we present a model based on twin support vector machine (TWSVM) for У site identification. The model combines a variety of feature representation techniques and uses the max-relevance and min-redundancy methods to obtain the optimum feature subset for training. The independent testing accuracy is improved by 3.4% in comparison to current advanced У site predictors. The outcomes demonstrate that our model has better generalization performance and improves the accuracy of У site identification. iPseU-TWSVM can be a helpful tool to identify У sites.

Indecision and delays are the parents of failure-taming them algorithmically by synthesizing delay-resilient control.

The possible interactions between a controller and its environment can naturally be modelled as the arena of a two-player game, and adding an appropriate winning condition permits to specify desirable behavior. The classical model here is the positional game, where both players can (fully or partially) observe the current position in the game graph, which in turn is indicative of their mutual current states. In practice, neither sensing and actuating the environment through physical devices nor data forwarding to and from the controller and signal processing in the controller are instantaneous. The resultant delays force the controller to draw decisions before being aware of the recent history of a play and to submit these decisions well before they can take effect asynchronously. It is known that existence of a winning strategy for the controller in games with such delays is decidable over finite game graphs and with respect to ω -regular objectives. The underlying reduction, however, is impractical for non-trivial delays as it incurs a blow-up of the game graph which is exponential in the magnitude of the delay. For safety objectives, we propose a more practical incremental algorithm successively synthesizing a series of controllers handling increasing delays and reducing the game-graph size in between. It is demonstrated using benchmark examples that even a simplistic explicit-state implementation of this algorithm outperforms state-of-the-art symbolic synthesis algorithms as soon as non-trivial delays have to be handled. We furthermore address the practically relevant cases of non-order-preserving delays and bounded message loss, as arising in actual networked control, thereby considerably extending the scope of regular game theory under delay.