Long and Diverse Text Generation with Planning-based Hierarchical Variational Model

Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.Comment: To appear in EMNLP 201

Effects of Compositional Tailoring on Drug Delivery Behaviours of Silica Xerogel/Polymer Core-shell Composite Nanoparticles

Conventional core-shell polymer nanoparticles usually exhibit a rapid release rate with their release kinetics mainly adjusted through changing composition of the polymer shells, limiting their applications for prolonged drug delivery. As a solution to these problems, silica xerogel/polymer core-shellstructured composite nanoparticles have been proposed. Different with our previous work centring on studying process variables, we here focused on investigating the effects of key compositional variables on essential properties of the composite nanoparticles. The drug release profiles (in vitro) were well interpreted by the Baker and Lonsdale model on a predicted two-stage basis. The first stage (\u3c1 day) was well controlled from 18.6% to 45.9%; the second stage (1–14 days) was tailored in a range from 28.7 to 58.2% by changing the composition of the silica xerogel cores and polymeric shells. A substantial achievement was reducing the release rate by more than 40 times compared with that of conventional polymer nanoparticles by virtue of the silica xerogel cores. A semi-empirical model was also established in the first attempt to describe the effects of polymer concentration and drug loading capacity on the size of the composite nanoparticles. All these results indicated that the composite nanoparticles are promising candidates for prolonged drug delivery applications

Effects of Compositional Tailoring on Drug Delivery Behaviours of Silica Xerogel/Polymer Core-shell Composite Nanoparticles

Conventional core-shell polymer nanoparticles usually exhibit a rapid release rate with their release kinetics mainly adjusted through changing composition of the polymer shells, limiting their applications for prolonged drug delivery. As a solution to these problems, silica xerogel/polymer core-shellstructured composite nanoparticles have been proposed. Different with our previous work centring on studying process variables, we here focused on investigating the effects of key compositional variables on essential properties of the composite nanoparticles. The drug release profiles (in vitro) were well interpreted by the Baker and Lonsdale model on a predicted two-stage basis. The first stage (\u3c1 day) was well controlled from 18.6% to 45.9%; the second stage (1–14 days) was tailored in a range from 28.7 to 58.2% by changing the composition of the silica xerogel cores and polymeric shells. A substantial achievement was reducing the release rate by more than 40 times compared with that of conventional polymer nanoparticles by virtue of the silica xerogel cores. A semi-empirical model was also established in the first attempt to describe the effects of polymer concentration and drug loading capacity on the size of the composite nanoparticles. All these results indicated that the composite nanoparticles are promising candidates for prolonged drug delivery applications

Binge-Like Exposure to Ethanol Enhances Morphine's Anti-nociception in B6 Mice

Elevation of the blood ethanol concentration (BEC) to > 80 mg/dL (17.4 mM) after binge drinking enhances inflammation in brain and neuroimmune signaling pathways. Morphine abuse is frequently linked to excessive drinking. Morphine exerts its actions mainly via the seven transmembrane G-protein-coupled mu opioid receptors (MORs). Opioid use disorders (OUDs) include combination of opioids with alcohol, leading to opioid overdose-related deaths. We hypothesized that binge drinking potentiates onset and progression of OUD. Using C57BL/6J (B6) mice, we first characterized time-dependent inflammatory gene expression change as molecular markers using qRT-PCR within 24 h after binge-like exposure to high-dose, high-concentration ethanol (EtOH). The mice were given one injection of EtOH (5 g/kg, 42% v/v, i.g.) and sacrificed at 2.5 h, 5 h, 7.5 h, or 24 h later. Inflammatory cytokines interleukin (IL)-1β, IL-6, and IL-18 were elevated in both the striatum (STr) and the nucleus accumbens (NAc) of the mice. We then investigated the expression profile of MOR in the STr at 2 min, 5 h, or 24 h after the first EtOH injection and at 24 h and 48 h after the third injection. This binge-like exposure to EtOH upregulated MOR expression in the STr and NAc, an effect that could enhance morphine's anti-nociception. Therefore, we examined the impact of binge-like exposure to EtOH on morphine's anti-nociception at the behavioral level. The mice were treated with or without 3-d binge-like exposure to EtOH, and the anti-nociceptive changes were evaluated using the hot-plate test 24 h after the final (3rd) EtOH injection with or without a cumulative subcutaneous dose (0, 0.1, 0.3, 1.0, and 3.0 mg/kg) of morphine at intervals of 30 min. The response curve of the mice given EtOH was shifted to the left, showing enhanced latency to response to morphine up to 3 mg/kg. Furthermore, co-treatment with the MOR antagonist naltrexone blocked morphine's anti-nociception in animals given either EtOH or saline. This confirms that MOR is involved in binge-like exposure to EtOH-induced changes in morphine's anti-nociception. Our results suggest that EtOH enhanced latency to analgesic response to morphine, and such effect might initiate the onset and progression of OUDs

Intraneuronal β-amyloid Accumulation: Aging HIV-1 Human and HIV-1 Transgenic Rat Brain

The prevalence of HIV-1 associated neurocognitive disorders (HAND) is significantly greater in older, relative to younger, HIV-1 seropositive individuals; the neural pathogenesis of HAND in older HIV-1 seropositive individuals, however, remains elusive. To address this knowledge gap, abnormal protein aggregates (i.e., β-amyloid) were investigated in the brains of aging (\u3e12 months of age) HIV-1 transgenic (Tg) rats. In aging HIV-1 Tg rats, double immunohistochemistry staining revealed abnormal intraneuronal β-amyloid accumulation in the prefrontal cortex (PFC) and hippocampus, relative to F344/N control rats. Notably, in HIV-1 Tg animals, increased β-amyloid accumulation occurred in the absence of any genotypic changes in amyloid precursor protein (APP). Furthermore, no clear amyloid plaque deposition was observed in HIV-1 Tg animals. Critically, β-amyloid was co-localized with neurons in the cortex and hippocampus, supporting a potential mechanism underlying synaptic dysfunction in the HIV-1 Tg rat. Consistent with these neuropathological findings, HIV-1 Tg rats exhibited prominent alterations in the progression of temporal processing relative to control animals; temporal processing relies, at least in part, on the integrity of the PFC and hippocampus. In addition, in post-mortem HIV-1 seropositive individuals with HAND, intraneuronal β-amyloid accumulation was observed in the dorsolateral PFC and hippocampal dentate gyrus. Consistent with observations in the HIV-1 Tg rat, no amyloid plaques were found in these post-mortem HIV-1 seropositive individuals with HAND. Collectively, intraneuronal β-amyloid aggregation observed in the PFC and hippocampus of HIV-1 Tg rats supports a potential factor underlying HIV-1 associated synaptodendritic damage. Further, the HIV-1 Tg rat provides a biological system to model HAND in older HIV-1 seropositive individuals

Intraneuronal β-Amyloid Accumulation: Aging HIV-1 Human and HIV-1 Transgenic Rat Brain

The prevalence of HIV-1 associated neurocognitive disorders (HAND) is significantly greater in older, relative to younger, HIV-1 seropositive individuals; the neural pathogenesis of HAND in older HIV-1 seropositive individuals, however, remains elusive. To address this knowledge gap, abnormal protein aggregates (i.e., β-amyloid) were investigated in the brains of aging (\u3e12 months of age) HIV-1 transgenic (Tg) rats. In aging HIV-1 Tg rats, double immunohistochemistry staining revealed abnormal intraneuronal β-amyloid accumulation in the prefrontal cortex (PFC) and hippocampus, relative to F344/N control rats. Notably, in HIV-1 Tg animals, increased β-amyloid accumulation occurred in the absence of any genotypic changes in amyloid precursor protein (APP). Furthermore, no clear amyloid plaque deposition was observed in HIV-1 Tg animals. Critically, β-amyloid was co-localized with neurons in the cortex and hippocampus, supporting a potential mechanism underlying synaptic dysfunction in the HIV-1 Tg rat. Consistent with these neuropathological findings, HIV-1 Tg rats exhibited prominent alterations in the progression of temporal processing relative to control animals; temporal processing relies, at least in part, on the integrity of the PFC and hippocampus. In addition, in post-mortem HIV-1 seropositive individuals with HAND, intraneuronal β-amyloid accumulation was observed in the dorsolateral PFC and hippocampal dentate gyrus. Consistent with observations in the HIV-1 Tg rat, no amyloid plaques were found in these post-mortem HIV-1 seropositive individuals with HAND. Collectively, intraneuronal β-amyloid aggregation observed in the PFC and hippocampus of HIV-1 Tg rats supports a potential factor underlying HIV-1 associated synaptodendritic damage. Further, the HIV-1 Tg rat provides a biological system to model HAND in older HIV-1 seropositive individuals

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

BackgroundAcute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources.MethodsIn this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model.ResultsEleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain.ConclusionThe ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources