Dataset for the proteomic and transcriptomic analyses of perivitelline fluid proteins in Pomacea snail eggs

This article describes how the proteomic and transcriptomic data were produced during a study of the reproductive proteins of Pomacea maculata, an aquatic apple snail laying colorful aerial eggs, and provides public access to the data. The data are related to a research article titled ?An integrated proteomic and transcriptomic analysis of perivitelline fluid proteins in a freshwater gastropod laying aerial eggs? (Mu et al., 2017) [1]. RNA was extracted from the albumen gland and other tissues and sequenced on an Illumina Hiseq. 2000. The assembled transcriptome was translated into protein sequences and then used for protein identification. Proteins from the perivitelline fluid of P. maculata were separated in SDS-PAGE and analyzed by LTQ-Orbitrap Elite coupled to an Easy-nLC. The translated transcriptome data are provided in this article. Proteomic data (.raw file format) are available via ProteomeXchange with the identifier PXD006718.Fil: Mu, Huawei. Hong Kong Baptist University; ChinaFil: Sun, Jin. Hong Kong University; ChinaFil: Heras, Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Bioquímicas de La Plata "Prof. Dr. Rodolfo R. Brenner". Universidad Nacional de la Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata ; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; ArgentinaFil: Chu, Ka Hou. Chinese University of Hong Kong; ChinaFil: Qiu, Jian Wen. Hong Kong Baptist University; Chin

An Approach to the Production of Soluble Protein from a Fungal Gene Encoding an Aggregation-Prone Xylanase in Escherichia coli

The development of new procedures and protocols that allow researchers to obtain recombinant proteins is of fundamental importance in the biotechnology field. A strategy was explored to overcome inclusion-body formation observed when expressing an aggregation-prone fungal xylanase in Escherichia coli. pHsh is an expression plasmid that uses a synthetic heat-shock (Hsh) promoter, in which gene expression is regulated by an alternative sigma factor (σ32). A derivative of pHsh was constructed by fusing a signal peptide to xynA2 gene to facilitate export of the recombinant protein to the periplasm. The xylanase was produced in a soluble form. Three factors were essential to achieving such soluble expression of the xylanase: 1) the target gene was under the control of the Hsh promoter, 2) the gene product was exported into the periplasm, and 3) gene expression was induced by a temperature upshift. For the first time we report the expression of periplasmic proteins under the control of an Hsh promoter regulated by σ32. One unique feature of this approach was that over 200 copies of the Hsh promoter in an E. coli cell significantly increased the concentration of σ32. The growth inhibition of the recombinant cells corresponded to an increase in the levels of soluble periplasmic protein. Therefore, an alternative protocol was designed to induce gene expression from pHsh-ex to obtain high levels of active soluble enzymes

Comparison of the nutrient resorption stoichiometry of Quercus variabilis Blume growing in two sites contrasting in soil phosphorus content

Key message Foliar phosphorus (P) resorption in Quercus variabilis Blume was significantly lower at a P-rich than at a P-deficient site. Moreover, P resorption strongly decreased, and nitrogen: phosphorus and carbon: phosphorus resorption ratios increased with soil P content. This demonstrates a strong link between foliar P resorption and P content in soils, and emphasizes the importance of P resorption in leaves of trees growing in soils with contrasted P content. Context Subtropical ecosystems are generally characterized by P-deficient soils. However, P-rich soils develop in phosphate rock areas. Aims We compared the patterns of nutrient resorption, in terms of ecological stoichiometry, for two sites naturally varying in soil P content. Methods The resorption efficiency (percentage of a nutrient recovered from senescing leaves) and proficiency (level to which nutrient concentration is reduced in senesced leaves) of 12 elements were determined in two oak (Q. variabilis) populations growing at a P-rich or a P-deficient site in subtropical China. Results P resorption efficiency dominated the intraspecific variation in nutrient resorption between the two sites. Q. variabilis exhibited a low P resorption at the P-rich site and a high P resorption at the P-deficient site. Both P resorption efficiency and proficiency strongly decreased with soil P content only and were positively related to the N:P and C:P ratios in green and senesced leaves. Moreover, resorption efficiency ratios of both N:P and C:P were positively associated with soil P. Conclusion These results revealed a strong link between P resorption and P stoichiometry in response to a P deficiency in the soil, and a single- and limiting-element control pattern of P resorption. Hence, these results provide new insights into the role of P resorption in plant adaptations to geologic variations of P in the subtropics.Peer reviewe

Utilizing Explainable AI for improving the Performance of Neural Networks

Nowadays, deep neural networks are widely used in a variety of fields that have a direct impact on society. Although those models typically show outstanding performance, they have been used for a long time as black boxes. To address this, Explainable Artificial Intelligence (XAI) has been developing as a field that aims to improve the transparency of the model and increase their trustworthiness. We propose a retraining pipeline that consistently improves the model predictions starting from XAI and utilizing state-of-the-art techniques. To do that, we use the XAI results, namely SHapley Additive exPlanations (SHAP) values, to give specific training weights to the data samples. This leads to an improved training of the model and, consequently, better performance. In order to benchmark our method, we evaluate it on both real-life and public datasets. First, we perform the method on a radar-based people counting scenario. Afterward, we test it on the CIFAR-10, a public Computer Vision dataset. Experiments using the SHAP-based retraining approach achieve a 4% more accuracy w.r.t. the standard equal weight retraining for people counting tasks. Moreover, on the CIFAR-10, our SHAP-based weighting strategy ends up with a 3% accuracy rate than the training procedure with equal weighted samples.Comment: accepted at ICMLA 202

Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have abundant features and labels. In contrast, silent nodes only have incomplete features and rare labels, e.g., the description and political tendency of politicians (vocal) are abundant while not for ordinary people (silent) on the twitter's social network. Predicting the silent majority remains a crucial yet challenging problem. However, most existing message-passing based GNNs assume that all nodes belong to the same domain, without considering the missing features and distribution-shift between domains, leading to poor ability to deal with VS-Graph. To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes. Specifically, we design the domain-adapted "feature completion and message passing mechanism" for node representation learning while preserving domain difference. And a knowledge transferable classifier based on KL-divergence is followed. Comprehensive experiments on real-world scenarios (i.e., company financial risk assessment and political elections) demonstrate the superior performance of our method. Our source code has been open sourced.Comment: Paper was accepted by WWW202

Disrupting the Interaction between Retinoblastoma Protein and Raf-1 Leads to Defects in Progenitor Cell Proliferation and Survival during Early Inner Ear Development

The retinoblastoma protein (pRb) is required for cell-cycle exit of embryonic mammalian hair cells but is not required for hair cell fate determination and early differentiation, and this provides a strategy for hair cell regeneration by manipulating the pRb pathway. To reveal the mechanism of pRb functional modification in the inner ear, we compared the effects of attenuated pRb phosphorylation by an inhibitor of the Mitogen-Activated Protein (MAP) kinase pathway and an inhibitor of the Rb–Raf-1 interaction on cultured chicken otocysts. We demonstrated that the activity of pRb is correlated with its phosphorylation state, which is regulated by a newly established cell cycle-independent pathway mediated by the physical interaction between Raf-1 and pRb. The phosphorylation of pRb plays an important role during the early stage of inner ear development, and attenuated phosphorylation in progenitor cells leads to cell cycle arrest and increased apoptosis along with a global down-regulation of the genes involved in cell cycle progression. Our study provides novel routes to modulate pRb function for hair cell regeneration

Robust Recommender System: A Survey and Future Directions

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters "dirty" data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training against malicious attacks, and regularization, purification, self-supervised learning against natural noise. Additionally, we summarize evaluation metrics and common datasets used to assess robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to equip readers with a holistic understanding of robust recommender systems and spotlight pathways for future research and development

Small RNA Sequencing Reveals Regulatory Roles of MicroRNAs in the Development of Meloidogyne incognita

MicroRNAs (miRNAs) are an extensive class of small regulatory RNAs. Knowing the specific expression and functions of miRNAs during root-knot nematode (RKN) (Meloidogyne incognita) development could provide fundamental information about RKN development as well as a means to design new strategies to control RKN infection, a major problem of many important crops. Employing high throughput deep sequencing, we identified a total of 45 conserved and novel miRNAs from two developmental stages of RKN, eggs and J2 juveniles, during their infection of cotton (Gossypium hirsutum L.). Twenty-one of the miRNAs were differentially expressed between the two stages. Compared with their expression in eggs, two miRNAs were upregulated (miR252 and miRN19), whereas 19 miRNAs were downregulated in J2 juveniles. Nine miRNAs were expressed at high levels, with >1000 reads per mapped million (RPM) sequenced reads in both eggs and J2 juveniles (miR1, miR124, miR2-3p, miR252, miR279, miR57-5p, miR7904, miR87, and miR92). Three miRNAs were only expressed in eggs (miR4738, miRN3, and miRN5). These differentially expressed miRNAs may control RKN development by regulating specific protein-coding genes in pathways associated with RKN growth and development

Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method

The development of knowledge graph (KG) applications has led to a rising need for entity alignment (EA) between heterogeneous KGs that are extracted from various sources. Recently, graph neural networks (GNNs) have been widely adopted in EA tasks due to GNNs' impressive ability to capture structure information. However, we have observed that the oversimplified settings of the existing common EA datasets are distant from real-world scenarios, which obstructs a full understanding of the advancements achieved by recent methods. This phenomenon makes us ponder: Do existing GNN-based EA methods really make great progress? In this paper, to study the performance of EA methods in realistic settings, we focus on the alignment of highly heterogeneous KGs (HHKGs) (e.g., event KGs and general KGs) which are different with regard to the scale and structure, and share fewer overlapping entities. First, we sweep the unreasonable settings, and propose two new HHKG datasets that closely mimic real-world EA scenarios. Then, based on the proposed datasets, we conduct extensive experiments to evaluate previous representative EA methods, and reveal interesting findings about the progress of GNN-based EA methods. We find that the structural information becomes difficult to exploit but still valuable in aligning HHKGs. This phenomenon leads to inferior performance of existing EA methods, especially GNN-based methods. Our findings shed light on the potential problems resulting from an impulsive application of GNN-based methods as a panacea for all EA datasets. Finally, we introduce a simple but effective method: Simple-HHEA, which comprehensively utilizes entity name, structure, and temporal information. Experiment results show Simple-HHEA outperforms previous models on HHKG datasets.Comment: 11 pages, 6 figure