Radiology report generation with a learned knowledge base and multi-modal alignment.

In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing radiology reports is a heavy burden for radiologists. To this end, we present an automatic, multi-modal approach for report generation from a chest x-ray. Our approach, motivated by the observation that the descriptions in radiology reports are highly correlated with specific information of the x-ray images, features two distinct modules: (i) Learned knowledge base: To absorb the knowledge embedded in the radiology reports, we build a knowledge base that can automatically distill and restore medical knowledge from textual embedding without manual labor; (ii) Multi-modal alignment: to promote the semantic alignment among reports, disease labels, and images, we explicitly utilize textual embedding to guide the learning of the visual feature space. We evaluate the performance of the proposed model using metrics from both natural language generation and clinic efficacy on the public IU-Xray and MIMIC-CXR datasets. Our ablation study shows that each module contributes to improving the quality of generated reports. Furthermore, the assistance of both modules, our approach outperforms state-of-the-art methods over almost all the metrics. Code is available at https://github.com/LX-doctorAI1/M2KT.

Knowledge matters: Chest radiology report generation with general and specific knowledge.

Automatic chest radiology report generation is critical in clinics which can relieve experienced radiologists from the heavy workload and remind inexperienced radiologists of misdiagnosis or missed diagnose. Existing approaches mainly formulate chest radiology report generation as an image captioning task and adopt the encoder-decoder framework. However, in the medical domain, such pure data-driven approaches suffer from the following problems: 1) visual and textual bias problem; 2) lack of expert knowledge. In this paper, we propose a knowledge-enhanced radiology report generation approach introduces two types of medical knowledge: 1) General knowledge, which is input independent and provides the broad knowledge for report generation; 2) Specific knowledge, which is input dependent and provides the fine-grained knowledge for chest X-ray report generation. To fully utilize both the general and specific knowledge, we also propose a knowledge-enhanced multi-head attention mechanism. By merging the visual features of the radiology image with general knowledge and specific knowledge, the proposed model can improve the quality of generated reports. The experimental results on the publicly available IU-Xray dataset show that the proposed knowledge-enhanced approach outperforms state-of-the-art methods in almost all metrics. And the results of MIMIC-CXR dataset show that the proposed knowledge-enhanced approach is on par with state-of-the-art methods. Ablation studies also demonstrate that both general and specific knowledge can help to improve the performance of chest radiology report generation.

Persistence of Immunity in Adults after 1, 5 and 10 Years with Recombinant Hepatitis B Vaccine in Beijing in 2010-2020.

The persistence of immunity after hepatitis B vaccination is still under investigation in adults. In Chaoyang District, Beijing, people who were aged ≥ 18 years and completely immunized with HBV vaccine according to the standard procedure (0-1-6 months) were enrolled. Three groups were set for 1 (Y1), 5 (Y5) and 10 (Y10) years after the hepatitis B vaccination. The following data was collected and analyzed: antibody against hepatitis B virus surface antigen(anti-HBs) positive rates and geometric mean concentration (GMC) between the different compared groups through questionnaires and laboratory detection, including hepatitis B virus surface antigen (HBsAg), anti-HBs and antibody against hepatitis B virus core antigen(anti-HBc). All 600 subjects completed the questionnaires and serological tests. Among all subjects, the positive rates of HBsAg, anti-HBs and anti-HBc were 0, 70.5% (423/600) and 2.5% (15/600), respectively. The anti-HBs positive rates in Y1, Y5 and Y10 groups were 86.5% (173/200), 71.0% (142/200) and 54.0% (108/200) (χ2 = 50.8, p < 0.001) and showed a linear decreasing trend year by year (trend χ2 = 50.7, p < 0.001). The GMC in Y1, Y5 and Y10 groups were 296.6 mIU/mL, 51.6 mIU/mL and 25.5 mIU/mL (H = 64.8, p < 0.001), respectively. The anti-HBs positive rates and GMC decreased rapidly after the vaccination of adults against hepatitis B. Screening after 5-10 years and booster vaccination for the unprotected population is recommended.

[4 + 1 + 1] Tandem Cyclization Reaction Involving Isocyanides: Access to 2-(Trifluoromethyl)quinazolin-4(3H)-imines.

A palladium-catalyzed three-component reaction of isocyanides, 2,2,2-trifluoro-N-(2-iodophenyl)acetimidoyl chlorides, and amines for the one-pot synthesis of 2-(trifluoromethyl)quinazolin-4(3H)-imines was described. The protocol features a wide substrate scope, high efficiency, and readily available raw materials.

Aligning Source Visual and Target Language Domains for Unpaired Video Captioning.

Training supervised video captioning model requires coupled video-caption pairs. However, for many targeted languages, sufficient paired data are not available. To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language. To solve the task, a natural choice is to employ a two-step pipeline system: first utilizing video-to-pivot captioning model to generate captions in pivot language and then utilizing pivot-to-target translation model to translate the pivot captions to the target language. However, in such a pipeline system, 1) visual information cannot reach the translation model, generating visual irrelevant target captions; 2) the errors in the generated pivot captions will be propagated to the translation model, resulting in disfluent target captions. To address these problems, we propose the Unpaired Video Captioning with Visual Injection system (UVC-VI). UVC-VI first introduces the Visual Injection Module (VIM), which aligns source visual and target language domains to inject the source visual information into the target language domain. Meanwhile, VIM directly connects the encoder of the video-to-pivot model and the decoder of the pivot-to-target model, allowing end-to-end inference by completely skipping the generation of pivot captions. To enhance the cross-modality injection of the VIM, UVC-VI further introduces a pluggable video encoder, i.e., Multimodal Collaborative Encoder (MCE). The experiments show that UVC-VI outperforms pipeline systems and exceeds several supervised systems. Furthermore, equipping existing supervised systems with our MCE can achieve 4% and 7% relative margins on the CIDEr scores to current state-of-the-art models on the benchmark MSVD and MSR-VTT datasets, respectively.

Development of high-performance liquid chromatographic for quality and authenticity control of Chinese propolis.

A RP-high-performance liquid chromatography (HPLC) method was developed for quality control of Chinese propolis by simultaneous analysis of 12 flavonoids and 8 phenolic acids. The results showed that vanillic acid, rutin, myricetin, and luteolin were not detected in all of the analyzed propolis and poplar tree gum samples. The caffeic acid, ferulic acid and p-coumaric acid were not detected in poplar tree gum but were detected in propolis, which suggest that they are practical indexes of distinguishing propolis from poplar tree gum. The flavonoid profiles of poplar tree gum were found to be similar to those of propolis, which are dominated by pinobanksin, pinocembrin, 3-O-acetylpinobanksin, chrysin, and galangin. Therefore, the proposed method could be applied to exclude poplar tree gum from propolis with cafferic acid, ferulic acid, and p-coumaric acid as qualitative markers, and distinguish poplar source resin from other illegal substances, and evaluate the quality grading of poplar-type propolis with pinobanksin, pinocembrin, 3-O-acetylpinobanksin, chrysin, and galangin as qualitative and quantitative markers.

Catalytic dynamic spectrofluorimetry determination of trace antimony using new type arsenoxylphenylazo rhodanine.

A precise, simple, new spectrofluorimetry method is proposed for determination of trace antimony which is based on the reaction between potassium periodate and the new type fluorescent reagent 3-o-chlorophenyl-5-(2'- arsenoxylphenylazo) rhodanine (2ClRAAP). The possible mechanism is proposed. The fluorescence intensity is investigated to be sharply enhanced by the oxidation of 3-o-chlorophenyl-5-(2'-arsenoxylphenylazo) rhodanine by potassium periodate with antimony as catalyst in the buffer medium of potassium hydrogen phthalate-sodium hydroxide (pH 5.2). Under the optimum conditions the great increase of fluorescence intensity has a linear relationship against the concentration of antimony in the range of 0.2-10 microg L(-1) with a detection limit of 1.65 x 10(-10) g mL(-1). This proposed method led to the satisfied determination of antimony in environment water.