Set Of Seeds Research Articles

Seq2Set2Seq: A Two-stage Disentangled Method for Reply Keyword Generation in Social Media

Social media produces large amounts of content every day. How to predict the potential influences of the contents from a social reply feedback perspective is a key issue that has not been explored. Thus, we propose a novel task named reply keyword prediction in social media, which aims to predict the keywords in the potential replies in as many aspects as possible. One prerequisite challenge is that the accessible social media datasets labeling such keywords remain absent. To solve this issue, we propose a new dataset, 1 to study the reply keyword prediction in social media. This task could be seen as a single-turn dialogue keyword prediction for open-domain dialogue system. However, existing methods for dialogue keyword prediction cannot be adopted directly, which has two main drawbacks. First, they do not provide an explicit mechanism to model topic complementarity between keywords which is crucial in social media to controllably model all aspects of replies. Second, the collocations of keywords are not explicitly modeled, which also makes it less controllable to optimize for fine-grained prediction since the context information is much less than that in dialogue. To address these issues, we propose a two-stage disentangled framework, which can optimize the complementarity and collocation explicitly in a disentangled fashion. In the first stage, we use a sequence-to-set paradigm via multi-label prediction and determinantal point processes, to generate a set of keyword seeds satisfying the complementarity. In the second stage, we adopt a set-to-sequence paradigm via seq2seq model with the keyword seeds guidance from the set, to generate the more-fine-grained keywords with collocation. Experiments show that this method can generate not only a more diverse set of keywords but also more relevant and consistent keywords. Furthermore, the keywords obtained based on this method can achieve better reply generation results in the retrieval-based system than others.

Identification of Ossnrk1a-1 Regulated Genes Associated with Rice Immunity and Seed Set.

Sucrose non-fermenting-1-related protein kinase-1 (SnRK1) is a highly conserved serine-threonine kinase complex regulating plants' energy metabolisms and resistance to various types of stresses. However, the downstream genes regulated by SnRK1 in these plant physiological processes still need to be explored. In this study, we found that the knockout of OsSnRK1a resulted in no obvious defects in rice growth but notably decreased the seed setting rate. The ossnrk1a mutants were more sensitive to blast fungus (Magnaporthe oryzae) infection and showed compromised immune responses. Transcriptome analyses revealed that SnRK1a was an important intermediate in the energy metabolism and response to biotic stress. Further investigation confirmed that the transcription levels of OsNADH-GOGAT2, which positively controls rice yield, and the defense-related gene pathogenesis-related protein 1b (OsPR1b) were remarkably decreased in the ossnrk1a mutant. Moreover, we found that OsSnRK1a directly interacted with the regulatory subunits OsSnRK1β1 and OsSnRK1β3, which responded specifically to blast fungus infection and starvation stresses, respectively. Taken together, our findings provide an insight into the mechanism of OsSnRK1a, which forms a complex with specific β subunits, contributing to rice seed set and resistance by regulating the transcription of related genes.

Influence maximization based on threshold models in hypergraphs.

Influence maximization problem has received significant attention in recent years due to its application in various domains, such as product recommendation, public opinion dissemination, and disease propagation. This paper proposes a theoretical analysis framework for collective influence in hypergraphs, focusing on identifying a set of seeds that maximize influence in threshold models. First, we extend the message passing method from pairwise networks to hypergraphs to accurately describe the activation process in threshold models. Then, we introduce the concept of hypergraph collective influence (HCI) to measure the influence of nodes. Subsequently, we design an algorithm, HCI-TM, to select the influence maximization set, taking into account both node and hyperedge activation. Numerical simulations demonstrate that HCI-TM outperforms several competing algorithms in synthetic and real-world hypergraphs. Furthermore, we find that HCI can be used as a tool to predict the occurrence of cascading phenomena. Notably, we find that the HCI-TM algorithm works better for larger average hyperdegrees in Erdös-Rényi hypergraphs and smaller power-law exponents in scale-free hypergraphs.

Development of Methods for Improving Flowering and Seed Set of Diverse Germplasm in Cassava Breeding

Development of Methods for Improving Flowering and Seed Set of Diverse Germplasm in Cassava Breeding

Priority populations’ experiences of isolation, quarantine and distancing for COVID-19: protocol for a longitudinal cohort study (Optimise Study)

IntroductionLongitudinal studies can provide timely and accurate information to evaluate and inform COVID-19 control and mitigation strategies and future pandemic preparedness. The Optimise Study is a multidisciplinary research platform established...

Crossing and Setting of Hybrid Seeds in the Creation of Chromosome-Substituted Cotton Lines g. Hirsutum l.

A comparative study of the indicators of crossability and setting of hybrid and backcross seeds F1, F1BC1, F1BC2, obtained from crossings of monosomic and monotelodisomic lines of cotton G.hirsutum L. with the donor line Pima 3-79 of the species G.barbadense L. and aneuploid backcross hybrids, was found to be a linear decrease of these indicators in some hybrids, as well as their increase, as well as the alternation of indicators of crossability and setting in different hybrid generations. The study of chromosome conjugation in hybrid monosomic F1 obtained from crossing monosomic lines with the donor line Pima 3-79 of the species G. barbadense L., as well as backcross monosomic F1BC1, F1BC2 with the replacement of specific chromosomes of the cotton genome, revealed normal chromosome conjugation with the formation of 25 bivalents and one univalent of different size in all studied PMCs in most hybrids.

CoDiS: Community Detection via Distributed Seed Set Expansion on Graph Streams

Community detection has been (and remains) a very important topic in several fields. From marketing and social networking to biological studies, community detection plays a key role in advancing research in many different fields. Research on this topic originally looked at classifying nodes into discrete communities (non-overlapping communities) but eventually moved forward to placing nodes in multiple communities (overlapping communities). Unfortunately, community detection has always been a time-inefficient process, and datasets are too large to realistically process them using traditional methods. Because of this, recent methods have turned to parallelism and graph stream models, where the edge list is accessed one edge at a time. However, all these methods, while offering a significant decrease in processing time, still have several shortcomings. We propose a new parallel algorithm called community detection with seed sets (CoDiS), which solves the overlapping community detection problem in graph streams. Initially, some nodes (seed sets) have known community structures, and the aim is to expand these communities by processing one edge at a time. The innovation of our approach is that it splits communities among the parallel computation workers so that each worker is only updating a subset of all the communities. By doing so, we decrease the edge processing throughput and decrease the amount of time each worker spends on each edge. Crucially, we remove the need for every worker to have access to every community. Experimental results show that we are able to gain a significant improvement in running time with no loss of accuracy.

Acid Rain and Seed Germination: A Predictive Model Using ML-based CART Algorithm

The impact of acid rain on the germination of seeds is a significant concern in agricultural and environmental studies. Acid rain, characterized by elevated acidity levels due to pollutants like sulfur dioxide and nitrogen oxides, can adversely affect the germination process of various plant species. The objective of this study was to evaluate the impact of simulated acid rain (SAR) on the germination of Brinjal (Solanum melongena Linn.) and Cowpea (Vigna unguiculata ssp. cylindrica L. Walpers) crops. The experiments were conducted using eight plastic trays of approximately 25 cm. x 30 cm dimensions. Four trays were used for experiments with brinjal seeds (Set I), while the other four were used for cowpea seeds (Set II). One tray from each set was used as positive control and treated with normal pH 5.6, while the other three trays from each batch were treated with SAR solutions of pH 4.5, 3.5, and 2.5. Brinjal seed germination percentage and seed vigor were inferior to Cowpea seeds. The seeds treated with SAR (pH 4.5, 3.5, and 2.5) showed hindered seed germination. Furthermore, a more significant inhibitory effect was observed at lower pH values. The mean germination percentage of seeds was highest for standard SAR (pH 5.6) in the case of Brinjal seeds, while it was recorded lowest for Cowpea seeds. The results indicate that plants do not respond uniformly to SAR. To investigate the behavior of the simulated acid rain data, a Machine Learning-based Decision Tree Algorithm was employed to identify and optimize conditions. Cowpea was predicted to get 95% seed germination, whereas brinjal would only be 64% in acid rain of pH value 5.05 for 36 hours. In conclusion, utilizing a Machine Learning-based CART algorithm has provided valuable insights into predicting the germination behavior of seeds under the influence of acid rain.

Germination parameters of Boswellia sacra Flueck. seeds and the possibility of their improvement by sorting

The aim of this study was to 1/ evaluate basic germination characteristics of Boswellia sacra seeds in two years of maturation (2018 and 2020), which differed in weather conditions before flowering and fruiting, 2/ find out the reason why the majority of the seeds do not germinate, and 3/ to determine whether seed sorting by floating effectively removes empty seeds. Two seed sets collected in 2018 and 2020 were germinated in trials to evaluate basic germination characteristics for unsorted seeds and seeds after 7- and 24-hours of soaking in water. The higher germination rate of seeds was reached for the set collected in 2018 compared to the one from 2020; sedimented up to 7 h of soaking in water seed set had the highest germination rate; seeds germinated quickly; most seeds germinated up to 4 days from the beginning of the germination test. To support the best result of germination rate of any set of seeds, we recommend seed soaking in water for about 7 h to sort out empty seeds which proportion could be relatively high.

Chaining for accurate alignment of erroneous long reads to acyclic variation graphs.

Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit [Garrison et al. (Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875-9)] is a popular aligner of short reads, GraphAligner [Rautiainen and Marschall (GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 2020;21:253-28)] is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12-17% more reads, and 21-28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22, and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph [Li et al. (The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020;21:265-19.)] and minichain [Chandra and Jain (Sequence to graph alignment using gap-sensitive co-linear chaining. In: Proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2023). Springer, 2023, 58-73.)] obtain an accuracy of <60% on this setting. GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address.

Жизнеспособность и оплодотворяющая способность пыльцы различного срока хранения у линий-восстановителей фертильности рапса озимого в тепличных условиях

To fasten breeding process of winter rapeseed hybrids, in V.S. Pustovoit All-Russian Research Institute of Oil Crops, a phytotron-greenhouse complex is used to obtain additional generations of plants. We studied the viability and fertilizing ability of pollen of nine lines-restorers of fertility and four maternal CMS-lines with purpose to determine optimal conditions for the hybridization in a greenhouse, which provide maximal amount of seeds. It was stated pollen of only open flowers of lines-restorers of fertility possesses the high viability – about 80–98%. When storing in a darkness at a room air temperature of 22–24 °C during 24 hour, pollen loses the viability by 2.0–2.5 times depending on a genotype. However, such important lowering of the pollen viability does not influence significantly on setting of seeds and seedpods. The seed formation in hybrids obtained after crossing with paternal lines having the viability of a day pollen from 15 to 52% did not distinguished significantly. The acceptable level of seed formation in hybrids was observed when using pollen with a storage period until five days. When using pollen storing for ten days at a temperature of 22–24 °C, seeds were not formed at hybrid combinations. There were detected the significant differences by the fertilizing ability between studied lines-restorers of fertility. So it is necessary to pay attention to this trait when developing commercial hybrids. The lowered fertilizing ability of pollen of the concrete restorer of fertility will influence the yield of hybrid seeds on the hybridization plots.

Popularity Ratio Maximization: Surpassing Competitors through Influence Propagation

In this paper, we present an algorithmic study on how to surpass competitors in popularity by strategic promotions in social networks. We first propose a novel model, in which we integrate the Preferential Attachment (PA) model for popularity growth with the Independent Cascade (IC) model for influence propagation in social networks called PA-IC model. In PA-IC, a popular item and a novice item grab shares of popularity from the natural popularity growth via the PA model, while the novice item tries to gain extra popularity via influence cascade in a social network. The popularity ratio is defined as the ratio of the popularity measure between the novice item and the popular item. We formulate Popularity Ratio Maximization (PRM) as the problem of selecting seeds in multiple rounds to maximize the popularity ratio in the end. We analyze the popularity ratio and show that it is monotone but not submodular. To provide an effective solution, we devise a surrogate objective function and show that empirically it is very close to the original objective function while theoretically, it is monotone and submodular. We design two efficient algorithms, one for the overlapping influence and non-overlapping seeds (across rounds) setting and the other for the non-overlapping influence and overlapping seed setting, and further discuss how to deal with other models and problem variants. Our empirical evaluation further demonstrates that our proposed method consistently achieves the best popularity promotion compared to other methods. Our theoretical and empirical analyses shed light on the interplay between influence maximization and preferential attachment in social networks.

A New Sparse Data Clustering Method Based On Frequent Items

Large, sparse categorical data is a natural way to represent complex data like sequences, trees, and graphs. Such data is prevalent in many applications, e.g., Criteo released a terabyte size click log data of 4 billion records with millions of dimensions. While most existing clustering algorithms like k-Means work well on dense, numerical data, there exist relatively few algorithms that can cluster sets of sparse categorical features. In this paper, we propose a new method called k-FreqItems that performs scalable clustering over high-dimensional, sparse data. To make clustering results easily interpretable, k-FreqItems is built upon a novel sparse center representation called FreqItem which will choose a set of high-frequency, non-zero dimensions to represent the cluster. Unlike most existing clustering algorithms, which adopt Euclidean distance as the similarity measure, k-FreqItems uses the popular Jaccard distance for comparing sets. Since the efficiency and effectiveness of k-FreqItems are highly dependent on an initial set of representative seeds, we introduce a new randomized initialization method, SILK, to deal with the seeding problem of k-FreqItems. SILK uses locality-sensitive hash (LSH) functions for oversampling and identifies frequently co-occurred data in LSH buckets to determine a set of promising seeds, allowing k-FreqItems to converge swiftly in an iterative process. Experimental results over seven real-world sparse data sets show that the SILK seeding is around 1.1\sim3.2× faster yet more effective than the state-of-the-art seeding methods. Notably, SILK scales up well to a billion data objects on a commodity machine with 4 GPUs. The code is available at https://github.com/HuangQiang/k-FreqItems.

Female Reproductive Potential and 3x by 2x Breeding Approach for Hybrid Seeds Development in Sukali Ndizi Genotype (Musa genome AAB)

Sukali Ndizi is one of the most popular local desert banana cultivars in Uganda. The crop is affected by a number of diseases and pests. Genetic improvement of the crop by hybridization may be hindered by very low seed set and poor seed germination. The main characteristics for assessing the female reproductive potential (seed set, seed quality and embryo germination of hybrid seeds) and 3x by 2x breeding approach were assessed in crosses of eleven different male diploid parents either wild or improved diploids with Sukali Ndizi landrace as the female parent for a period of 5 consecutive years planted in pollination blocks at National Agricultural Research Laboratories Kawanda (NARL). The month of pollination did not show any pattern throughout the pollination period but the crosses showed considerable variability in seed set, seed quality, and seed germination for different male diploids used. The water gravity test which differentiated the extracted seeds as sunken/viable and floating/nonviable seeds showed that a substantial amount of seed was floating and the sunken externally characterized by black hard integuments was only 39% with a range = 24%–60% which contained embryos, of which 22% (range = 0–37%) germinated. The increase in non-viable seed suggested that ovule abortion in Sukali Ndizi which mainly involved embryo and endosperm abortions was the cause of limited seed germination and the paternity of the zygotes was a major factor underlying abortions. Flow cytometric analysis of nuclear DNA content was used to estimate ploidy levels of the progenies with results showing that using 3x by 2x breeding approach differing levels of ploidy were achieved [3x (288), 4x (61), 5x (14) and 2x (2)] signifying the predominance of 3x progenies and the presence of 5x and 4x conforming to the possibility of sexual polyploids. Although pollination of Sukali Ndizi can be done all around the year, the seed set both quantity and quality and germination were very poor, necessitating the development of an efficient regeneration protocol for zygotic embryos at varying maturity stages to increase the recovery of hybrids.

GROWTH, DEVELOPMENT, YIELD AND ROOT AND SEED SETTING OF WINTER WHEAT AND REPROVED OIL CROP

GROWTH, DEVELOPMENT, YIELD AND ROOT AND SEED SETTING OF WINTER WHEAT AND REPROVED OIL CROP

The Potential Effects of Kinetin Implementation on Hybrid Rice Seed Production under Water Deficit

Drought is one of the main abiotic stresses responsible for reducing crop yields worldwide. In hybrid rice production, cytoplasmic male sterility (CMS) displays an alternative plan for producing high-yielding hybrid rice depending on the hybrid vigor. Kinetin (Kin) has an essential role during the early phase of grain setting by regulating cell division, assimilate flow, and osmotic modification under water deficit. Growth, floral, and yield-related traits were evaluated in two CMS lines under five irrigation intervals and two concentrations of the phytohormone kinetin. Our study was conducted to explore the effects of irrigation intervals (continuous flooding (CF), irrigation every six (I6), nine (I9), twelve (I12), and fifteen (I15) days, and kinetin exogenously applied (control, 15 mg L−1, and 30 mg L−1) on growth, floral, and yield-related traits. Growth traits (i.e., number of leaves (NOL), days to complete leaf number (DCLN), days to heading (DTH, 50%), flag leaf area (FLA), and plant height (PH)), floral traits (i.e., duration of spikelet opening (DSO), panicle length (PL), spikelet opening angle (SOA), and panicle exertion (PE)), and seed yield traits (i.e., seed set (SS), panicle weight (PW), seed yield (SY), harvest index (HI), and number of fertile panicles per hill (NFP)) in two CMS lines. Implementation of kinetin displayed the highest significant and positive values for all growth, floral, and yield-related traits when compared to the control (average of applied irrigation intervals). Applying 30 mg L−1 kinetin positively enhanced the growth traits (i.e., NOL, FLA, and PH with 4.1%, 5%, and 3%, respectively), floral traits (i.e., PL, PE, SOA, and DSO with 5.4, 5.7, 5.9, and 5.4%, respectively), and yield-related traits (i.e., PW, SS, SY, HI, and NFP with 22%, 17%, 14%, 14.5%, and 15%, with the same sequence) compared to non-treated plants. Consequently, exogenous foliar spray of kinetin could be an effective process in minimizing the harmful effects (the reduction in PW, SS, SY, HI, and NFP recorded 41%, 61%, 45%, 30%, and 48%, respectively, under I15 conditions when compared to CF) of water deficit in hybrid rice and increasing seed production.

Performance evaluation of secondary triploid Musa hybrids from in-vitro and ex-vitro derived propagules

This experiment was carried out at the High Rainfall Station of the International Institute of Tropical Agriculture (IITA) in Onne, Rivers State, Nigeria, to investigate if secondary triploid plantains established using in-vitro excised zygotic embryo propagules and tissue culture will differ significantly from those established by using ex-vitro propagules such as seeds obtained from open or hand pollinated parents. Seeds of secondary triploid plantain hybrids were obtained by crossing tetraploid female hybrids TMP4x 2796-5 and TMP4x 4698-1 by hand pollination with pollen from known diploid males TMP2x 1297-3 and TMP2x 1448-1. Another set of these female hybrids were exposed to open pollination. At maturity, seeds from ripened mature fruits from both methods of pollination had their zygotic embryos extracted and cultured in-vitro. Another set of seeds were grown ex-vitro in the soil. Both in-vitro and ex-vitro derived propagules obtained from open and hand pollinated parents were raised in the tissue culture laboratory and the nursery respectively. Three replications of the 12 treatments of eight-week-old secondary triploids were planted in the field at 3m x 2m spacing using an RCB (randomized complete block) design. Data collected were: number of days to 50% flowering (DTF), time to fruit filling (TFF), plant height and number of leaves at 50% flowering, bunch weight, number of hands/bunches, fingers/ bunch and total yield. Collected data were analysed using ANOVA in RCB design at (P = 0.05) while means were compared with Duncan’s Multiple Range Test at (P = 0.05). Inconsistent individual genotypically significant differences were found in some phenological and vegetative characters, yield constituents and total yield between ex-vitro and in-vitro derived secondary triploids indicating no definitive pattern in responses of the secondary triploids. However, only days to flowering (DTF) showed consistent significant notable differences out of all these traits. Generally, the ex-vitro derived secondary triploids flowered significantly later than in-vitro derived ones. The secondary triploids from open pollinated parental crosses flowered significantly earlier than those obtained from the hand pollinated parental crosses.

Nurseries of the revolution: international seed trials and the transformation of staple food crops

ABSTRACT This paper examines the role of nurseries in the Green Revolution. Nurseries were sets of seeds assembled to test particular traits and sent to breeders around the world to trial. Focusing on wheat nurseries of the 1960s and 70s, I highlight the materials that were central to their distribution, as well as the breeders who planted them. Through this analysis, I trouble the common narrative of the Green Revolution as emerging from centers of international agricultural science, showing the networks of expertise, seed transfer, field trials, and data collection that underpinned this process of agricultural transformation.

A Novel miRNA in Rice Associated with the Low Seed Setting Rate Symptom of Rice Stripe Virus.

MicroRNAs play key regulatory roles in plant development. The changed pattern of miRNA expression is involved in the production of viral symptoms. Here, we showed that a small RNA, Seq119, a putative novel microRNA, is associated with the low seed setting rate, a viral symptom of rice stripe virus (RSV)-infected rice. The expression of Seq 119 was downregulated in RSV-infected rice. The overexpression of Seq119 in transgenic rice plants did not cause any obvious phenotypic changes in plant development. When the expression of Seq119 was suppressed in rice plants either by expressing a mimic target or by CRISPR/Cas editing, seed setting rates were extremely low, similar to the effects of RSV infection. The putative targets of Seq119 were then predicted. The overexpression of the target of Seq119 in rice caused a low seed setting rate, similar to that in Seq119-suppressed or edited rice plants. Consistently, the expression of the target was upregulated in Seq119-suppressed and edited rice plants. These results suggest that downregulated Seq119 is associated with the low seed setting rate symptom of the RSV in rice.

Establishment of seed biopriming in salt stress mitigation of rice plants by mangrove derived Bacillus sp.

Salinity stress is one of the growing challenges for agro-ecosystems. Present study aimed to explore the role of halotolerant bacteria in alleviation of salinity stressed rice plants. Bacillus sp. strain PnD was isolated from Indian Sundarban Mangrove Forest, conferred plant growth promoting (PGP) traits like indole 3-acetic acid (IAA) production, phosphate solubilization, siderophore production but had negative effect of 1-aminoacyclopropane 1-carboxylate (ACC) deaminase activity. Rice seeds (Oryza sativa L.) of Amal-Mana variety were subjected to salinity stress for 24 h by being immersing in 1% NaCl. Another set of seeds were soaked in a broth cultured with Bacillus sp strain PnD, containing 1% NaCl in the media, denoted as bioprimed condition. The seeds that were soaked in distilled water had no salt stress and were not primed, served as control. All the treatments were assigned randomly to the seeds and each treatment was replicated three times resulting in nine experimental units. Completely randomized design was adopted in this experiment. Overall results showed bioprimed seeds soaked in 1% NaCl for 24 h had significantly higher germination percentage, seedling growth, photosynthetic pigments content compared to control and non-primed salinity exposed plants. Scanning electron micrograph revealed root colonization of bacteria in bioprimed seedlings, tissue distortion was also noted during salt stress. The study revealed that Bacillus sp. strain PnD is a potential plant growth promoting bacterium (PGPB), and the seed biopriming technique can efficiently mitigate inhibitory effects of salinity stress in rice plants.