Joint Generator-Ranker Learning for Natural Language Generation

Generate-then-rank is a widely used mechanism for text generation, where a generator produces multiple text candidates and a ranker chooses the best one among the text candidates. However, existing methods usually train the generator and the ranker individually, neglecting the mutual feedback that could further enhance the generation quality. To tackle this limitation, we propose JGR, a novel joint training algorithm that integrates the generator and the ranker in a single framework. JGR optimizes the generator with a hybrid objective that combines data likelihood and ranker reward, and trains the ranker with a contrastive loss that compares the generator outputs. By iteratively updating the generator and the ranker, JGR can effectively harmonize their learning and enhance their quality jointly. We evaluate JGR on various text generation tasks and demonstrate that it surpasses existing methods on four public datasets across three common generation scenarios. Our code and models are publicly available at https://github.com/microsoft/ProphetNet/tree/master/JGR

Tissue Stresses in Stented Coronary Arteries with Different Geometries: Effect of the Relation Between Stent Length and Lesion Length

In-stent restenosis after stent deployment remains an obstruction in the long-term benefits of stenting. This study sought to investigate the influence of the relation between stent length and lesion length on the mechanics of the arterial wall with different geometries, including straight and tapered vessels. Results showed that when the length of the stent was longer than the lesion length, the maximum stress in plaque and vessel increased as the length of stent increased. When the length of the stent was shorter than the lesion length, the vessel stress induced by stent inflation was lower; both ends of the stenosis plaque could not be fully expanded. When the length of the stent was equal to the lesion length, the plaque and vessel stress induced by stent inflation was minimal, and stent foreshortening was minimal. Compared with the straight vessel, the stent implantation in the tapered vessel with the same stent length resulted in greater stress in vessel and plaque, an increased stent recoil, and a decreased stent foreshortening. When the length of the stent is equal to lesion length, it may be the reasonable choice for straight vessels and tapered vessels. Conclusions drawn from this article can help surgeons to choose appropriate stent lengths

Impact of intestinal microbiota on metabolic toxicity and potential detoxification of amygdalin

Amygdalin (Amy) is metabolized into cyanide in vivo, which may lead to fatal poisoning after oral administration. The defense mechanisms against toxic cyanide have not yet been adequately studied. In this study, comparative toxicokinetics study of Amy was performed in normal and pseudo germ-free rats. The efficiency of cyanide release was significant higher in normal group when given a single oral dose of 440 mg/kg (50% median lethal dose). Thiocyanate, the detoxification metabolite, was firstly detected in feces, caecum, and intestinal microbiota incubation enzymic system. The results suggest intestinal microbiota is involved in bidirectional regulation of toxicity and detoxification of Amy. We further identified the species related to cyanogenesis of Amy with metagenomic sequencing, such as Bifidobacterium pseudolongum, Marvinbryantia formatexigens, and Bacteroides fragilis. Functional analysis of microbiota reveals the detoxification potential of intestinal microbiota for cyanide. Sulfurtransferase superfamily, such as rhodanese, considered as main detoxification enzymes for cyanide, are largely found in Coriobacteriaceae bacterium, Butyricicoccus porcorum, Akkermansia muciniphila, etc. Besides, cyanoamino acid metabolism pathway dominated by Escherichia coli may contribute to the detoxification metabolism of cyanide. In summary, intestinal microbiota may be the first line of defense against the toxicity induced by Amy

A machine learning-based model for predicting distant metastasis in patients with rectal cancer

BackgroundDistant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential.MethodThe study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley’s Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer.ResultThe study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis. Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, perineural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0.859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0.900, and precision was 0.880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0.810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer.ConclusionThe study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validatio

Structuring Interaction Networks Between Epiphytic Bryophytes and Their Hosts in Yunnan, SW China

peer reviewedEcological networks are commonly applied to depict general patterns of biotic interactions, which provide tools to understand the mechanism of community assembly. Commensal interactions between epiphytes and their hosts are a major component of species interactions in forest canopies; however, few studies have investigated species assemblage patterns and network structures of epiphyte–host interactions, particularly non-vascular epiphytes in different types of forest. To analyze the characteristics of network structures between epiphytes and their hosts, composition and distribution of epiphytic bryophytes were investigated from 138 host individuals using canopy cranes in a tropical lowland seasonal rain forest (TRF) and a subtropical montane moist evergreen broad-leaved forest (STF), in Southwest China. We structured binary networks between epiphytic bryophytes and their hosts in these two forests, which presented 329 interactions in the TRF and 545 interactions in the STF. Compared to TRF, the bryophyte–host plant networks were more nested but less modular in the STF. However, both forests generally exhibited a significantly nested structure with low levels of specialization and modularity. The relatively high nestedness may stabilize the ecological networks between epiphytic bryophytes and their hosts. Nevertheless, the low modularity in epiphyte–host networks could be attributed to the lack of co-evolutionary processes, and the low degree of specialization suggests that epiphytes are less likely to colonize specific host species. Vertical distribution of the bryophyte species showed structured modules in the tree basal and crown zones, probably attributing to the adaptation to microclimates within a host individual. This study highlights the nested structure of commensal interaction between epiphytic bryophytes and host trees, and provides a scientific basis to identify key host tree species for conservation and management of biodiversity in forest ecosystems

Maize straw application as an interlayer improves organic carbon and total nitrogen concentrations in the soil profile: A four-year experiment in a saline soil

Soil salinization is a critical environmental issue restricting agricultural production. Deep return of straw to the soil as an interlayer (at 40 cm depth) has been a popular practice to alleviate salt stress. However, the legacy effects of straw added as an interlayer at different rates on soil organic carbon (SOC) and total nitrogen (TN) in saline soils still remain inconclusive. Therefore, a four-year (2015–2018) field experiment was conducted with four levels (i.e., 0, 6, 12 and 18 Mg ha–1) of straw returned as an interlayer. Compared with no straw interlayer (CK), straw addition increased SOC concentration by 14–32 and 11–57% in the 20–40 and 40–60 cm soil layers, respectively. The increases in soil TN concentration (8–22 and 6–34% in the 20–40 and 40–60 cm soil layers, respectively) were lower than that for SOC concentration, which led to increased soil C:N ratio in the 20–60 cm soil depth. Increases in SOC and TN concentrations in the 20–60 cm soil layer with straw addition led to a decrease in stratification ratios (0–20 cm:20–60 cm), which promoted uniform distributions of SOC and TN in the soil profile. Increases in SOC and TN concentrations were associated with soil salinity and moisture regulation and improved sunflower yield. Generally, compared with other treatments, the application of 12 Mg ha–1 straw had higher SOC, TN and C:N ratio, and lower soil stratification ratio in the 2015–2017 period. The results highlighted that legacy effects of straw application as an interlayer were maintained for at least four years, and demonstrated that deep soil straw application had a great potential for improving subsoil fertility in salt-affected soils.publishedVersio

Physics perspectives of heavy-ion collisions at very high energy

Heavy-ion collisions at very high colliding energies are expected to produce a quark-gluon plasma (QGP) at the highest temperature obtainable in a laboratory setting. Experimental studies of these reactions can provide an unprecedented range of information on properties of the QGP at high temperatures. We report theoretical investigations of the physics perspectives of heavy-ion collisions at a future high-energy collider. These include initial parton production, collective expansion of the dense medium, jet quenching, heavy-quark transport, dissociation and regeneration of quarkonia, photon and dilepton production. We illustrate the potential of future experimental studies of the initial particle production and formation of QGP at the highest temperature to provide constraints on properties of strongly interaction matter.Comment: 35 pages in Latex, 29 figure