Controllable Multi-Objective Re-ranking with Policy Hypernetworks

Multi-stage ranking pipelines have become widely used strategies in modern recommender systems, where the final stage aims to return a ranked list of items that balances a number of requirements such as user preference, diversity, novelty etc. Linear scalarization is arguably the most widely used technique to merge multiple requirements into one optimization objective, by summing up the requirements with certain preference weights. Existing final-stage ranking methods often adopt a static model where the preference weights are determined during offline training and kept unchanged during online serving. Whenever a modification of the preference weights is needed, the model has to be re-trained, which is time and resources inefficient. Meanwhile, the most appropriate weights may vary greatly for different groups of targeting users or at different time periods (e.g., during holiday promotions). In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights. In this way, CMR is enabled to adapt the preference weights according to the environment changes in an online manner, without retraining the models. Moreover, we classify practical business-oriented tasks into four main categories and seamlessly incorporate them in a new proposed re-ranking model based on an Actor-Evaluator framework, which serves as a reliable real-world testbed for CMR. Offline experiments based on the dataset collected from Taobao App showed that CMR improved several popular re-ranking models by using them as underlying models. Online A/B tests also demonstrated the effectiveness and trustworthiness of CMR

The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets

BackgroundMethamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients.MethodFecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features.ResultsMUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients.ConclusionIn conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings

Physiological, Proteomic, and Resin Yield-Related Genes Expression Analysis Provides Insights into the Mechanisms Regulating Resin Yield in Masson Pine

Masson pine (Pinus massoniana Lamb.) is an important resin-producing conifer species in China. Resin yield is a highly heritable trait and varies greatly among different genotypes. However, the mechanisms regulating the resin yield of masson pine remain largely unknown. In this study, physiological, proteomic, and gene expression analysis was performed on xylem tissues of masson pine with high and low resin yield. Physiological investigation showed that the activity of terpene synthase, as well as the contents of soluble sugar, jasmonic acid (JA), methyl jasmonate (MeJA), gibberellins (GA1, GA4, GA9, GA19, and GA20), indole-3-acetic acid (IAA), and abscisic acid (ABA) were significantly increased in the high yielder, whereas sucrose and salicylic acid (SA) were significantly decreased compared with the low one. A total of 2984 differentially expressed proteins (DEPs) were identified in four groups, which were mainly enriched in the biosynthesis of secondary metabolites, protein processing in the endoplasmic reticulum, carbohydrate metabolism, phytohormone biosynthesis, glutathione metabolism, and plant-pathogen interaction. Integrated physiological and proteomic analysis revealed that carbohydrate metabolism, terpenoid biosynthesis, resistance to stress, as well as JA and GA biosynthesis and signaling, play key roles in regulating resin yield. A series of proteins associated with resin yield, e.g., terpene synthase proteins (TPSs), ATP-binding cassette transporters (ABCs), glutathione S-transferase proteins (GSTs), and heat shock proteins (HSPs), were identified. Resin yield-related gene expression was also associated with resin yield. Our study unveils the implicated molecular mechanisms regulating resin yield and is of pivotal significance to breeding strategies of high resin-yielding masson pine cultivars

Identification of Genes and Metabolic Pathways Involved in Resin Yield in Masson Pine by Integrative Analysis of Transcriptome, Proteome and Biochemical Characteristics

Masson pine (Pinus massoniana L.) is one of the most important resin-producing tree species in southern China. However, the molecular regulatory mechanisms of resin yield are still unclear in masson pine. In this study, an integrated analysis of transcriptome, proteome, and biochemical characteristics from needles of masson pine with the high and common resin yield was investigated. The results showed that chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl C), carotenoids (Car), glucose (Glu), gibberellin A9 (GA9), gibberellin A15 (GA15), and gibberellin A53 (GA53) were significantly increased, whereas fructose (Fru), jasmonic acid (JA), jasmonoyl-L-isoleucine (JA-ILE), gibberellin A1 (GA1), gibberellin A3 (GA3), gibberellin A19 (GA19), and gibberellin A24 (GA24) were significantly decreased in the high resin yield in comparison with those in the common one. The integrated analysis of transcriptome and proteome showed that chlorophyll synthase (chlG), hexokinase (HXK), sucrose synthase (SUS), phosphoglycerate kinase (PGK), dihydrolipoamide dehydrogenase (PDH), dihydrolipoamide succinyltransferase (DLST), 12-oxophytodienoic acid reductase (OPR), and jasmonate O-methyltransferases (JMT) were consistent at the transcriptomic, proteomic, and biochemical levels. The pathways of carbohydrate metabolism, terpenoid biosynthesis, photosynthesis, and hormone biosynthesis may play crucial roles in the regulation of resin yield, and some key genes involved in these pathways may be candidates that influence the resin yield. These results provide insights into the molecular regulatory mechanisms of resin yield and also provide candidate genes that can be applied for the molecular-assisted selection and breeding of high resin-yielding masson pine

Characterization of the Transcriptome and Gene Expression of Tetraploid Black Locust Cuttings in Response to Etiolation

Etiolation (a process of growing plants in partial or complete absence of light) promotes adventitious root formation in tetraploid black locust (Robinia pseudoacacia L.) cuttings. We investigated the mechanism underlying how etiolation treatment promotes adventitious root formation in tetraploid black locust and assessed global transcriptional changes after etiolation treatment. Solexa paired-end sequencing of complementary DNAs (cDNAs) from control (non-etiolated, NE) and etiolated (E) samples resulted in 107,564 unigenes. In total, 52,590 transcripts were annotated and 474 transcripts (211 upregulated and 263 downregulated) potentially involved in etiolation were differentially regulated. These genes were associated with hormone metabolism and response, photosynthesis, signaling pathways, and starch and sucrose metabolism. In addition, we also found significant differences of phytohormone contents, activity of following enzymes i.e., peroxidase, polyphenol oxidase and indole acetic acid oxidase between NE and E tissues during some cottage periods. The genes responsive to etiolation stimulus identified in this study will provide the base for further understanding how etiolation triggers adventitious roots formation in tetraploid black locus

Copy number variations of <i>LRRFIP1</i> gene and the relationship with growth traits in four Chinese sheep

CNVs (copy number variations) are the novel and common structural variants that could cover entire genes found in plenty of species. CNV may influence economically important traits or disease susceptibility in livestock species. Based on the whole genome resequencing results, we found that there was a CNV region on the LRRFIP1 gene. Then we used qPCR to detect the copy number type distribution in 553 individuals of four sheep breeds and used them for association analysis. The results showed that: (1) In the CKS, the sheep with gain type had a larger heart girth (p = 0.049). (2) For the HS, the CNV could significantly affect rump breadth (p = 0.037) and circumference of the cannon (p = 0.035). And the sheep with median type showed better performance in rump breadth and circumference of cannon. (3) At the STHS, the CNV was significantly correlated with chest width (p = 0.000) with loss type as the most favorable CNV type. Meanwhile, the best was the loss type, and the lowest was the median. (4) This CNV had no significant effect on the LTHS. So, the CNV of LRRFIP1 was related to the growth traits of these three sheep breeds and it may be used as a molecular marker for sheep.</p

Climatic humidity mediates the strength of the species richness-biomass relationship on the Mongolian Plateau steppe

The relationships between biodiversity and ecosystem functioning (BEF) have been extensively studied over past decades. However, the environmental factors affecting their relationships, and how their relationships vary under the influence of environmental factors, remain controversial. Studying the BEF relationships in natural/wild environments is of great significance for devising strategies in biodiversity conservation and ecosystem functioning. Using the data from 75 sites on the Mongolian Plateau steppe, we analyzed the relationship between species richness and biomass with general linear models (GLMs) and linear mixed models (LMMs), and analyzed the variation in the species richness-biomass relationships under environmental conditions by the partial least square path modeling (PLSPM). The results showed that de Martonne aridity index affected both species richness and community biomass in parallel, and that hydrothermal coupling conditions were more important direct impact factors for aboveground biomass. However, the significant species richness-biomass relationships became weaker when the effects of environmental factors (i.e. climate and soil properties) were present. Climate humidity was the most important factor in mediating the relationship between species richness and community biomass. Our research suggested that species richness-biomass relationships are weak in the natural grasslands of the Mongolian Plateau, and that this may be due to the differences in the regional-scale environment and changes in species interactions. We recommend that a more comprehensive understanding of the relationship between diversity and biomass requires further research within broader environmental gradients

Dissecting the Ultrafast Stepwise Bidirectional Proton Relay in a Blue-Light Photoreceptor

Proton relays through H-bond networks are essential in realizing the functionality of protein machines such as in photosynthesis and photoreceptors. It has been challenging to dissect the rates and energetics of individual proton-transfer steps during the proton relay. Here, we have designed a proton rocking blue light using a flavin (BLUF) domain with the flavin mononucleotide (FMN)–glutamic acid (E)–tryptophan (W) triad and have resolved the four individual proton-transfer steps kinetically using ultrafast spectroscopy. We have found that after the photo-induced charge separation forming FMN·–/E-COOH/WH·+, the proton first rapidly jumps from the bridging E-COOH to FMN– (τfPT2 = 3.8 ps; KIE = 1.0), followed by a second proton transfer from WH·+ to E-COO– (τfPT1 = 336 ps; KIE = 2.6) which immediately rocks back to W· (τrPT1 = 85 ps; KIE = 6.7), followed by a proton return from FMNH· to E-COO– (τrPT2 = 34 ps; KIE = 3.3) with the final charge recombination between FMN·– and WH·+ to close the reaction cycle. Our results revisited the Grotthuss mechanism on the ultrafast timescale using the BLUF domain as a paradigm protein