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

Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data

Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic coding (SC) network and the data adaptation (DA) network. The SC network learns how to extract and transmit the semantic information using a receiver-leading training process. By using the domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SC network can process without retraining. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution

Automatic Diagnosis for Prostate Cancer Using Run-Length Matrix Method

Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US1. Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist as normal tissue or prostatic carcinoma (PCa). The sample was then subsequently digitized at 50X magnification. We divided the digitized image into sub-regions of 20 X 20 pixels and classified each sub-region as normal or PCa by a texture analysis method. In the texture analysis, we computed texture features for each of the sub-regions based on the Gray-level Run-length Matrix(GL-RLM). Those features include LGRE, HGRE and RPC from the run-length matrix, mean and standard deviation of the pixel intensity. We utilized a feature selection algorithm to select a set of effective features and used a multi-layer perceptron (MLP) classifier to distinguish normal from PCa. In total, the whole histological image was divided into 42 PCa and 6280 normal regions. Three-fold cross validation results show that the proposed method achieves an average classification accuracy of 89.5% with a sensitivity and specificity of 90.48% and 89.49%, respectively

Anomalous photoluminescence in InP1-xBix

Low temperature photoluminescence (PL) from InP1-xBix thin films with Bi concentrations in the 0-2.49% range reveals anomalous spectral features with strong and very broad (linewidth of 700 nm) PL signals compared to other bismide alloys. Multiple transitions are observed and their energy levels are found much smaller than the band-gap measured from absorption measurements. These transitions are related to deep levels confirmed by deep level transient spectroscopy, which effectively trap free holes and enhance radiative recombination. The broad luminescence feature is beneficial for making super-luminescence diodes, which can theoretically enhance spatial resolution beyond 1 ?m in optical coherent tomography (OCT)

HIV-1 genetic diversity a challenge for AIDS vaccine development: A retrospective bibliometric analysis

Background: Despite recent advances in human immunodeficiency virus-1 (HIV-1) prevention, a fast, safe, and effective vaccine will probably be necessary to end the HIV/AIDS pandemic. This study was conducted to evaluate global research trends and map the key bibliometric indices in HIV-1 genetic diversity from 1998 to 2021.Methods: A comprehensive online search was conducted in the Web of Science Core Collection database to retrieve published literature on HIV-1 genetic diversity. Key bibliometric indicators were calculated and evaluated using HistCiteTM, Bibliometrix: An R-tool, and VOSviewer software for windows.Results: A total of 2,060 documents written by 9,201 authors and published in 250 journals were included in the final analysis. Year 2012 was the most productive year with 121 (5.87%) publications. The most prolific author was Shao Yiming (n = 74, 3.59%) from Chinese Center for Disease Control and Prevention. The United States of America was the highly contributing and influential country (n = 681, 33.05%). AIDS Research and Human Retroviruses was the most productive journal (n = 562, 27.2%). Network visualization shows that HIV-1 was the most widely used author keyword.Conclusion: This study provides global research trends and detailed information on HIV-1 genetic diversity. The amount of scientific literature on HIV-1 genetic diversity research has rapidly increased in the last two decades. The maximum number of articles on HIV-1 genetic diversity was published in developed countries; therefore, a scientific research collaboration among researchers and institutes in low-income countries should be promoted and supported

Anti-Apoptosis Effect of Astragaloside Iv on Alzheimer's Disease Rat Model via Enhancing the Expression of Bcl-2 And Bcl-Xl

The aim is to explore the protective effect of Astragaloside IV on Alzheimer’s disease (AD) in rats induced  by amyloid-ß peptide (Aß1-42) and its potential therapeutic mechanism. Methods: 50 Male Sprague Dawley  rats were divided into five groups (10 rats for each): control group, model group, treatment groups 1~3.  10μg Aß1-42 was injected bilaterally into the dorsal dentate gyrus of the hippocampus of rats in the model  and treatment groups to prepare the AD models. 24h after modeling, Astragaloside IV administration, with  different drug dosages of 20mg/(kg•day), 40mg/(kg•day) and 60mg/(kg•day), was performed by gastric  perfusion for rats in the treatment group 1~3. Later on, the cognitive ability of rats was examined by a series  of behavioral tests, and the expression of Bcl-2 and Bcl-xl in the hippocampus of rats was detected by the  fluorescein based Quantitative RT-PCR. Results: The spontaneous alternation test in a Y maze and Morris  water maze task have demonstrated that the repeated daily administration of Astragaloside IV at the doses  of 20mg/kg bw/day) (p<0.05), 40mg/kg bw/day) (p<0.01), and 60mg/kg bw/day) (p<0.01) significantly  ameliorated the impairment of performance caused by Aß1–42. Furthermore, Astragaloside IV also enhanced  the expression of Bcl-2 and Bcl-xl in hippocampal neurons of rats in a dosage-dependent manner. Conclusion:  These findings suggest that Astragaloside IV could alleviate cognitive impairment and enhance the  expression of Bcl-2 and Bcl-xl in hippocampus of rat models with AD.

Nanoscale distribution of Bi atoms in InP1-xBix

The nanoscale distribution of Bi in InPBi is determined by atom probe tomography and transmission electron microscopy. The distribution of Bi atoms is not uniform both along the growth direction and within the film plane. A statistically high Bi-content region is observed at the bottom of the InPBi layer close to the InPBi/InP interface. Bi-rich V-shaped walls on the (−111) and (1–11) planes close to the InPBi/InP interface and quasi-periodic Bi-rich nanowalls in the (1–10) plane with a periodicity of about 100 nm are observed. A growth model is proposed to explain the formation of these unique Bi-related nanoscale features. These features can significantly affect the deep levels of the InPBi epilayer. The regions in the InPBi layer with or without these Bi-related nanostructures exhibit different optical properties

Adjacent Slice Prostate Cancer Prediction to Inform MALDI Imaging Biomarker Analysis

Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the high confidence region to predict the true area of the tumor on the adjacent MALDI imaged slice