Regional testing of triploid hybrid clones of populus tomentosa.

BACKGROUND: Triploid Populus tomentosa, a timber tree species, has been widely planted in northern China owing to its potential high yields and high wood quality. Though genetic variances in growth traits and wood properties have been reported across several planting sites, regional testing of triploid hybrid clones of P. tomentosa has not been conducted on a large scale. RESULTS: Ten 5-year clonal trials were used to evaluate the inheritance of growth traits, to determine suitable deployment zones, and to identify optimal triploid clones at each experimental site to determine the clones that would be suitable at all sites. A total of 2,430 trees from nine triploid hybrid clones were sampled during the ten trials. The clonal and site effects and clone × site interactions were highly significant (P < 0.001) for all the studied growth and yield traits. The estimated repeatability of means for diameter at breast height (DBH) and tree height (H) was 0.83, which was slightly higher than for stem volume (SV) and estimated stand volume (ESV) (0.78). The Weixian (WX), Gaotang (GT), and Yanzhou (YZ) sites were each considered to be suitable deployment zones, and the Zhengzhou (ZZ), Taiyuan (TY), Pinggu (PG), and Xiangfen (XF) sites were found to be the optimal deployment zones. The TY and ZZ sites were the best discriminative environments, and the GT and XF sites were the best representative environments. GGE pilot analysis revealed that yield performance and stability were significantly different among all the studied triploid hybrid clones across the ten test sites. It was therefore necessary to develop a suitable triploid hybrid clone that could do well at each site. Taking into account both yield performance and stability, the triploid hybrid clone S2 was determined to be an ideal genotype. CONCLUSIONS: For triploid hybrid clones, the WX, GT, and YZ sites represented suitable deployment zones and the ZZ, TY, PG, and XF sites represented optimal deployment zones. Yield performance and stability were significantly different among all the studied triploid hybrid clones across the ten test sites. Developing a suitable triploid hybrid clone that could do well at all sites was therefore desirable.

In vitro induction of tetraploidy and its effects on phenotypic variations in Populus hopeiensis.

BACKGROUND: Artificial induction of polyploidy is the most common and effective way to improve the biological properties of Populus and develop new varieties of this tree. In this study, in order to confirm and expand earlier findings, we established a protocol using colchicine and based on an efficient shoot regeneration system of leaf blades to induce tetraploidy in vitro in three genotypes from diploid Populus hopeiensis. The stomatal characteristics, leaf blade size, and growth were evaluated for diploids and tetraploids of three genotypes. RESULTS: We found that genotype, preculture duration, colchicine concentration, and colchicine exposure time had highly significant effects on the tetraploid induction rate. The optimal protocol for inducing tetraploidy in P. hopeiensis was to preculture leaf blades for 7 days and then treat them for 4 days with 40 mg/L colchicine. The tetraploid induction rates of genotypes BT1, BT3, and BT8 were 21.2, 11.4 and 16.7%, respectively. A total of 136 tetraploids were identified by flow cytometry analysis and somatic chromosome counting. The stomatal length, width, and density of leaf blades significantly differed between diploid and tetraploid plants. Compared with their diploid counterparts, the tetraploids produced larger leaf blades and had a slower growth rate. Our findings further document the modified morphological characteristics of P. hopeiensis following whole-genome duplication (e.g., induced tetraploidy). CONCLUSIONS: We established a protocol for in vitro induction of tetraploidy from diploid leaf blades treated with colchicine, which can be applied to different genotypes of P. hopeiensis.

Microsporogenesis in the triploid hybrid 'Beilinxiongzhu 1#' and detection of primary trisomy in 2x × 3 × Populus hybrids.

BACKGROUND: Primary trisomy is a powerful genetic tool in plants. However, trisomy has not been detected in Populus as a model system for tree and woody perennial plant biology. RESULTS: In the present study, a backcross between Populus alba × Populus glandulosa 'YXY 7#' (2n = 2x = 38) and the triploid hybrid 'Beilinxiongzhu 1#' (2n = 3x = 57) based on the observation of microsporogenesis and an evaluation of the variations in pollen was conducted to create primary trisomy. Many abnormalities, such as premature migration of chromosomes, lagging of chromosomes, chromosome bridges, asymmetric separation, micronuclei, and premature cytokinesis, have been detected during meiosis of the triploid hybrid clone 'Beilinxiongzhu 1#'. However, these abnormal behaviors did not result in completely aborted pollen. The pollen diameter of the triploid hybrid clone 'Beilinxiongzhu 1#' is bimodally distributed, which was similar to the chromosomal number of the backcross progeny. A total of 393 progeny were generated. We provide a protocol for determining the number of chromosomes in aneuploid progeny, and 19 distinct simple sequence repeat (SSR) primer pairs covering the entire Populus genome were developed. Primary trisomy 11 and trisomy 17 were detected in the 2x × 3 x hybrid using the SSR molecular markers and counting of somatic chromosomes. CONCLUSIONS: Nineteen distinct SSR primer pairs for determining chromosomal number in aneuploid individuals were developed, and two Populus trisomies were detected from 2x × 3 x hybrids by SSR markers and somatic chromosome counting. Our findings provide a powerful genetic tool to reveal the function of genes in Populus.

Evaluation of Taraxacum mongolicum flavonoids in diets for Channa argus based on growth performance, immune responses, apoptosis and antioxidant defense system under lipopolysaccharide stress.

To ascertain the effects of Taraxacum mongolicum flavonoids (TMF) on the growth performance, digestive enzyme activity, immune indices, inflammatory response and antioxidant capacity of Channa argus, 400 C. argus with an average body weight of (8.08 ± 0.21) g were selected and divided randomly into four groups. They were fed with four experimental diets supplemented with TMF of 0 (control), 25, 50 and 100 mg/kg for 56 d, and then challenged with lipopolysaccharide (LPS) for 96 h, afterwards indices were detected. The results manifested that the addition of TMF above 50 mg/kg in the dietary could significantly improve the final body weight, WGR, SGR and PER of C. argus, while decreased FCR (P < 0.05). Similarly, the 50 mg/kg group had the highest activity of digestive enzymes (protease, lipase, amylase) in intestine and hepatopancreas, which were notably higher than those in the control group (P < 0.05). Nevertheless, 100 mg/kg group could effectively inhibit the liver and gut injury caused by LPS and reduce the contents of ALT and AST, LPS and LBP in serum. In the immune (LY, AKP, ACP, IgM, C3) and antioxidant (T-AOC, SOD, CAT, GSH-PX, GR, ASA, MDA) systems, 100 mg/kg groups were the optimal group, which were remarkably higher than those of the control group (P < 0.05). Additionally, the expression of genes revealed that 100 mg/kg group could noteworthy restrain the expression of pro-inflammatory factors (tnf-α, il-1ß, il-8) and pro-apoptosis (cas-3,8,9, p53, bax, bcl-2) related genes, up-regulate the expression of anti-inflammatory (il-10, tgf-ß) factors, antioxidant-related (nrf2, gpx, gst, cat) genes and heat shock proteins (hsp70, hsp90). Simultaneously, the survival rate of C. argus in the 100 mg/kg TMF-supplemented group was the highest after LPS challenge. Our results elucidate that dietary supplementation TMF protects C. argus from LPS-induced inflammatory injury, to ameliorate digestion, immune response, antioxidant status and apoptosis, implying that TMF could be regarded as an anti-inflammatory and antioxidant agent adding to aquatic animal feed.

Deep learning for pollen allergy surveillance from twitter in Australia.

BACKGROUND: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches. METHODS: The data was extracted from Twitter based on pre-defined keywords (i.e. 'hayfever' OR 'hay fever') throughout the period of 6 months, covering the high pollen season in Australia. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers. Standard evaluation metrics (i.e. Accuracy, Precision and Recall) were calculated for the results validation. Finally, visual correlation with weather variables was performed. RESULTS: The neural networks-based approach was able to correctly identify the implicit mentions of the symptoms and treatments, even unseen previously (accuracy up to 87.9% for GRU with GloVe embeddings of 300 dimensions). CONCLUSIONS: The system addresses the shortcomings of the conventional machine learning techniques with manual feature-engineering that prove limiting when exposed to a wide range of non-standard expressions relating to medical concepts. The case-study presented demonstrates an application of 'black-box' approach to the real-world problem, along with its internal workings demonstration towards more transparent, interpretable and reproducible decision-making in health informatics domain.

Polysaccharide from dandelion enriched nutritional composition, antioxidant capacity, and inhibited bioaccumulation and inflammation in Channa asiatica under hexavalent chromium exposure.

Taraxacum mongolicum polysaccharide (TMP) exhibits anti-inflammatory and antioxidant activity, making it an attractive candidate for aquatic-product-safety applications. Here, this study was aimed to investigate the effects of dietary TMP on the growth, nutritional composition, antioxidant capacity, bioaccumulation and inflammation in Channa asiatica under hexavalent chromium stress. The C. asiatica was randomly distributed into five groups: The first group served as the blank control group (CK), the subsequent groups were fed TMP-supplemented feed (0, 0.5, 1.0 and 2.0 g/kg), respectively, and exposed to waterborne Cr6+ for 28 days. Our results indicated that the TMP effectively increased (P < 0.05) C. asiatica muscle flavour amino acid, total free amino acids, monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), and EPA + DHA contents, enhanced positively antioxidant enzyme activity (GPX, SOD, CAT, T-AOC), reduced oxidative stress parameters (MDA, PC), and up-regulated antioxidant-related genes mRNA expression. Meanwhile, the appropriate amount of TMP supplementation also inhibited the bioaccumulation of Cr6+ in tissues and alleviated the inflammatory response (P < 0.05). Furthermore, sensory evaluation implied that the overall score of sashimi and cooked fillet in the 2.0 g/kg TMP group was the highest in the experimental group, second only to CK. In brief, these results elucidate that TMP-supplemented diets excellently ameliorated the growth, enriched nutritional composition and antioxidant capacity, and inhibited bioaccumulation and inflammation in C. asiatica exposed to waterborne Cr6+.

ChromosomeNet: A massive dataset enabling benchmarking and building basedlines of clinical chromosome classification.

Chromosome karyotyping analysis is a vital cytogenetics technique for diagnosing genetic and congenital malformations, analyzing gestational and implantation failures, etc. Since the chromosome classification as an essential stage in chromosome karyotype analysis is a highly time-consuming, tedious, and error-prone task, which requires a large amount of manual work of experienced cytogenetics experts. Many deep learning-based methods have been proposed to address the chromosome classification issues. However, two challenges still remain in current chromosome classification methods. First, most existing methods were developed by different private datasets, making these methods difficult to compare with each other on the same base. Second, due to the absence of reproducing details of most existing methods, these methods are difficult to be applied in clinical chromosome classification applications widely. To address the above challenges in the chromosome classification issue, this work builds and publishes a massive clinical dataset. This dataset enables the benchmarking and building chromosome classification baselines suitable for different scenarios. The massive clinical dataset consists of 126,453 privacy preserving G-band chromosome instances from 2763 karyotypes of 408 individuals. To our best knowledge, it is the first work to collect, annotate, and release a publicly available clinical chromosome classification dataset whose data size scale is also over 120,000. Meanwhile, the experimental results show that the proposed dataset can boost performance of existing chromosome classification models at a varied range of degrees, with the highest accuracy improvement by 5.39 % points. Moreover, the best baseline with 99.33 % accuracy reports state-of-the-art classification performance. The clinical dataset and state-of-the-art baselines can be found at https://github.com/CloudDataLab/BenchmarkForChromosomeClassification.

Gibberellins as a novel mutagen for inducing 2n gametes in plants.

The plant hormone gibberellin (GA) regulates many physiological processes, such as cell differentiation, cell elongation, seed germination, and the response to abiotic stress. Here, we found that injecting male flower buds with exogenous gibberellic acid (GA3) caused defects in meiotic cytokinesis by interfering with radial microtubule array formation resulting in meiotic restitution and 2n pollen production in Populus. A protocol for inducing 2n pollen in Populus with GA3 was established by investigating the effects of the dominant meiotic stage, GA3 concentration, and injection time. The dominant meiotic stage (F = 41.882, P < 0.001) and GA3 injection time (F = 172.466, P < 0.001) had significant effects on the frequency of induced 2n pollen. However, the GA3 concentration (F = 1.391, P = 0.253) did not have a significant effect on the frequency of induced 2n pollen. The highest frequency of GA3-induced 2n pollen (21.37%) was observed when the dominant meiotic stage of the pollen mother cells was prophase II and seven injections of 10 µM GA3 were given. Eighteen triploids were generated from GA3-induced 2n pollen. Thus, GA3 can be exploited as a novel mutagen to induce flowering plants to generate diploid male gametes. Our findings provide some new insight into the function of GAs in plants.

Neural attention with character embeddings for hay fever detection from twitter.

The paper aims to leverage the highly unstructured user-generated content in the context of pollen allergy surveillance using neural networks with character embeddings and the attention mechanism. Currently, there is no accurate representation of hay fever prevalence, particularly in real-time scenarios. Social media serves as an alternative to extract knowledge about the condition, which is valuable for allergy sufferers, general practitioners, and policy makers. Despite tremendous potential offered, conventional natural language processing methods prove limited when exposed to the challenging nature of user-generated content. As a result, the detection of actual hay fever instances among the number of false positives, as well as the correct identification of non-technical expressions as pollen allergy symptoms poses a major problem. We propose a deep architecture enhanced with character embeddings and neural attention to improve the performance of hay fever-related content classification from Twitter data. Improvement in prediction is achieved due to the character-level semantics introduced, which effectively addresses the out-of-vocabulary problem in our dataset where the rate is approximately 9%. Overall, the study is a step forward towards improved real-time pollen allergy surveillance from social media with state-of-art technology.

Feature selection for helpfulness prediction of online product reviews: An empirical study.

Online product reviews underpin nearly all e-shopping activities. The high volume of data, as well as various online review quality, puts growing pressure on automated approaches for informative content prioritization. Despite a substantial body of literature on review helpfulness prediction, the rationale behind specific feature selection is largely under-studied. Also, the current works tend to concentrate on domain- and/or platform-dependent feature curation, lacking wider generalization. Moreover, the issue of result comparability and reproducibility occurs due to frequent data and source code unavailability. This study addresses the gaps through the most comprehensive feature identification, evaluation, and selection. To this end, the 30 most frequently used content-based features are first identified from 149 relevant research papers and grouped into five coherent categories. The features are then selected to perform helpfulness prediction on six domains of the largest publicly available Amazon 5-core dataset. Three scenarios for feature selection are considered: (i) individual features, (ii) features within each category, and (iii) all features. Empirical results demonstrate that semantics plays a dominant role in predicting informative reviews, followed by sentiment, and other features. Finally, feature combination patterns and selection guidelines across domains are summarized to enhance customer experience in today's prevalent e-commerce environment. The computational framework for helpfulness prediction used in the study have been released to facilitate result comparability and reproducibility.