A new rainfall prediction model based on ICEEMDAN-WSD-BiLSTM and ESN

Precipitation, as an important indicator describing the evolution of the regional climate system, plays an important role in understanding the spatial and temporal distribution characteristics of regional precipitation. Scientific and accurate prediction of regional precipitation is helpful to provide theoretical basis for relevant departments to guide flood and drought control. To address the uncertainty and nonlinear characteristics of precipitation series, this paper uses the established improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-wavelet signal denoising (WSD)-bi-directional long short-term memory (BiLSTM), and echo state network (ESN) models to predict precipitation of four cities in southern Anhui Province. The BiLSTM is used to predict the high-frequency components and the ESN to predict the low-frequency components, thus avoiding the influence between the two neural network predictions. The results show that the ICEEMDAN-WSD-BiLSTM and ESN models are more accurate. The average relative error reached 2.64% and the NSE (Nash-Sutcliffe efficiency coefficient) was 0.91, which was significantly better than the other four models. The model reveals the temporal change pattern and evolution characteristics of future precipitation, guides flood prevention and mitigation, and has certain theoretical significance and application value for promoting regional sustainable development.

Dataset of white spot disease affected shrimp farmers disaggregated by the variables of farm site, environment, disease history, operational practices, and saline zones

The article presents the summary of a dataset related to the risks factors of white spot disease (WSD) of farmed shrimp (Penaeus monodon) in Khulna, Bagerhat and Satkhira districts of Bangladesh. This dataset was developed following two consecutive steps. In the first step, participatory rural appraisal tools were applied to get the conceptual framework for data collection regarding lists of farmers and the variables of the risk factors of WSD. In the second step, sampling of farmers, google featured questionnaire development, and mobile phone-assisted survey were carried out. The total surveyed farms were 233 consisting of 21 and 212 semi-intensive and extensive farms, respectively. The data were collected in the form of continuous, nominal and binary variables disaggregated by saline zones. The dataset contains some basic socio-economic data of shrimp farmers, farm characteristics, environmental attributes and disease history of shrimp farms. The dataset also has GPS coordinates of all the surveyed farms individually which are very useful for spatial analysis. In total, the dataset in MS Excel has 46 variables and attached as the supplementary material with this article.

Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text

INTRODUCTION

In this article, we evaluate a knowledge-based word sense disambiguation method that determines the intended concept associated with an ambiguous word in biomedical text using semantic similarity and relatedness measures. These measures quantify the degree of similarity or relatedness between concepts in the Unified Medical Language System (UMLS). The objective of this work is to develop a method that can disambiguate terms in biomedical text by exploiting similarity and relatedness information extracted from biomedical resources and to evaluate the efficacy of these measure on WSD.

METHOD

We evaluate our method on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms.

RESULTS

We show that information content-based measures derived from either a corpus or taxonomy obtain a higher disambiguation accuracy than path-based measures or relatedness measures on the MSH-WSD dataset.

AVAILABILITY

The WSD system is open source and freely available from http://search.cpan.org/dist/UMLS-SenseRelate/. The MSH-WSD dataset is available from the National Library of Medicine http://wsd.nlm.nih.gov.

Comparative analysis of conventional PCR and real-time PCR to diagnose shrimp WSD

The aims of this study were to standard and optimize a qPCR protocol with FAM-BHQ1 probe, and to compare its sensitivity against TaqMan qPCR and PCR methods to diagnose shrimp WSD. The FAM-BHQ1 qPCR presented higher clinical sensitivity and showed to be a robust alternative to detect WSSV in clinical samples.

Expression of Pisum sativum PsAO3 gene, which encodes an aldehyde oxidase utilizing abscisic aldehyde, is induced under progressively but not rapidly imposed drought stress

Aldehyde oxidase (AO; EC 1.2.3.1) catalyzes the final step of abscisic acid (ABA) biosynthesis, which is the oxidation of abscisic aldehyde (ABAld) to ABA. Gene expression analyses indicate that the stress-induced Pisum sativum PsAOγ isoform, which effectively uses ABAld as a substrate, is encoded by the PsAO3 gene. PsAO3 was heterologously expressed in Pichia pastoris and the recombinant PsAO3 protein revealed substrate preferences highly similar to the native PsAOγ protein present in the pea leaves and roots. Both proteins prefer indole-3-aldehyde and naphthaldehyde as substrates, although high activities against abscisic aldehyde and citral were also observed. The Km values of PsAO3 for naphthaldehyde and abscisic aldehyde (4.6 and 5.1 μM, respectively) were the lowest among the substrates tested. PsAO3 activity was almost completely inhibited by potassium cyanide, diphenyleneiodonium, and methanol. Rapidly imposed drought stress did not increase the level of PsAO3 mRNA or activity of any AO isoform, although an enhanced ABA accumulation and induction of PsNCED2 and -3 (9-cis-epoxycarotenoid dioxygenase; EC 1.13.11.51) expression, both in the pea roots and leaves, was observed. During a progressively induced drought, the level of PsAO3 transcript and PsAOγ activity increased significantly in the roots and leaves, whereas ABA accumulation occurred only in the leaves where it was accompanied by induction of the PsNCED3 expression. Therefore, we suppose that next to NCED, also AO (mainly PsAOγ) might be involved in regulation of the drought-induced ABA synthesis. However, while the "constitutive activity" of PsAOγ is sufficient for the fast generation of ABA under rapid drought stress, the enhanced PsAOγ activity is required for the progressive and long-term ABA accumulation in the leaves under progressive drought stress.

Identification and characterization of FaSOC1, a homolog of SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 from strawberry

A MADS-box gene SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) integrates multiple flowering signals to regulate floral transition in Arabidopsis. Strawberry (Fragaria spp.) is an economically important fruit crop, but its molecular control of flowering is largely unknown. In this study, a SOC1-like gene, FaSOC1, was isolated and characterized from strawberry. The open reading frame of FaSOC1 was 648bp, encoding a protein of 215 amino acids. Sequence alignment and phylogenetic analysis showed that the FaSOC1 protein contained a highly conserved MADS domain and a SOC1 motif, and that it was a member of the SOC1-like genes of dicots. The FaSOC1 protein mainly localized in the cytoplasm of onion epidermal cells and Arabidopsis protoplasts, and showed no transcriptional activation activity in yeast cells. Under the floral induction conditions, the expression of FaSOC1 increased during the first 2weeks of short-day treatment, but declined dramatically during three to 4weeks. FaSOC1 was highly expressed in reproductive organs, including shoot apices, floral buds, flowers, stamens and sepals. Overexpression of FaSOC1 in wild-type Arabidopsis caused early flowering and upregulated the expression of flowering time genes LFY and AP1. In addition, the yeast two-hybrid and BiFC assays confirmed that FaSOC1 could interact with AGL24. In conclusion, these results suggest that FaSOC1 is a flowering promoter in strawberry.