Medical Dialogue Generation via Dual Flow Modeling

Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. Since most patients cannot precisely describe their symptoms, dialogue understanding is challenging for MDS. Previous studies mainly addressed this by extracting the mentioned medical entities as critical dialogue history information. In this work, we argue that it is also essential to capture the transitions of the medical entities and the doctor's dialogue acts in each turn, as they help the understanding of how the dialogue flows and enhance the prediction of the entities and dialogue acts to be adopted in the following turn. Correspondingly, we propose a Dual Flow enhanced Medical (DFMed) dialogue generation framework. It extracts the medical entities and dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow, respectively. We employ two sequential models to encode them and devise an interweaving component to enhance their interactions. Experiments on two datasets demonstrate that our method exceeds baselines in both automatic and manual evaluations.Comment: Accepted as Findings of ACL 202

RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning

Automating radiology report generation can significantly alleviate radiologists' workloads. Previous research has primarily focused on realizing highly concise observations while neglecting the precise attributes that determine the severity of diseases (e.g., small pleural effusion). Since incorrect attributes will lead to imprecise radiology reports, strengthening the generation process with precise attribute modeling becomes necessary. Additionally, the temporal information contained in the historical records, which is crucial in evaluating a patient's current condition (e.g., heart size is unchanged), has also been largely disregarded. To address these issues, we propose RECAP, which generates precise and accurate radiology reports via dynamic disease progression reasoning. Specifically, RECAP first predicts the observations and progressions (i.e., spatiotemporal information) given two consecutive radiographs. It then combines the historical records, spatiotemporal information, and radiographs for report generation, where a disease progression graph and dynamic progression reasoning mechanism are devised to accurately select the attributes of each observation and progression. Extensive experiments on two publicly available datasets demonstrate the effectiveness of our model.Comment: Accepted by Findings of EMNLP 202

The History, Mechanism, and Clinical Application of Auricular Therapy in Traditional Chinese Medicine

Auricular therapy includes acupuncture, electroacupuncture, acupressure, lasering, cauterization, moxibustion, and bloodletting in the auricle. For 2500 years, people have employed auricular therapy for treating diseases, but the methods have been limited to bloodletting and cauterization. Only after 1957, the international scientific community became aware that the map of the ear resembles an inverted fetus, its introduction has led to auricular acupuncture (AA) becoming a more systemic approach, and, following the identification and standardization of more precise points, AA has been employed in clinical applications. The mechanisms of AA are considered to have a close relationship with the autonomic nervous system, the neuroendocrine system, neuroimmunological factors, neuroinflammation, and neural reflex, as well as antioxidation. Auricular therapy has been applied, for example, for pain relief, for the treatment of epilepsy, anxiety, and obesity, and for improving sleep quality. However, the mechanisms and evidence for auricular therapy warrant further study

Plant Species Diversity along a Precipitation Gradient in Temperate Grasslands of China and Mongolia

Variations in species diversity can be linked to several ecological gradients (Huston 1994). Plant functional type is characterized by an adaption of plants to certain ecological conditions (Galan de Mera et al. 1999). In addition, patterns of species richness along an environmental gradient might be more interpretable by considering both species richness of different functional types and total species richness (Pausas and Austin 2001). Water availability generally signifies total precipitation available to support plant growth (Adler and Levine 2007), and its temporal distribution is the main driver of species composition and species diversity in arid and semi-arid environments (Shmida and Wilson 1985; Kutiel et al. 2000). Therefore, understanding how precipitation influences species diversity at a spatial scale will be critical for predicting the impacts of altered precipitation on vegetation patterns. This study aimed to examine the vegetation response to a spatial precipitation gradient in temperature grassland in China and Mongolia

Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning

In recent years, text-to-video retrieval methods based on CLIP have experienced rapid development. The primary direction of evolution is to exploit the much wider gamut of visual and textual cues to achieve alignment. Concretely, those methods with impressive performance often design a heavy fusion block for sentence (words)-video (frames) interaction, regardless of the prohibitive computation complexity. Nevertheless, these approaches are not optimal in terms of feature utilization and retrieval efficiency. To address this issue, we adopt multi-granularity visual feature learning, ensuring the model's comprehensiveness in capturing visual content features spanning from abstract to detailed levels during the training phase. To better leverage the multi-granularity features, we devise a two-stage retrieval architecture in the retrieval phase. This solution ingeniously balances the coarse and fine granularity of retrieval content. Moreover, it also strikes a harmonious equilibrium between retrieval effectiveness and efficiency. Specifically, in training phase, we design a parameter-free text-gated interaction block (TIB) for fine-grained video representation learning and embed an extra Pearson Constraint to optimize cross-modal representation learning. In retrieval phase, we use coarse-grained video representations for fast recall of top-k candidates, which are then reranked by fine-grained video representations. Extensive experiments on four benchmarks demonstrate the efficiency and effectiveness. Notably, our method achieves comparable performance with the current state-of-the-art methods while being nearly 50 times faster

ICON: Improving Inter-Report Consistency of Radiology Report Generation via Lesion-aware Mix-up Augmentation

Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports. In this paper, we emphasize another crucial quality that it should possess, i.e., inter-report consistency, which refers to the capability of generating consistent reports for semantically equivalent radiographs. This quality is even of greater significance than the overall report accuracy in terms of ensuring the system's credibility, as a system prone to providing conflicting results would severely erode users' trust. Regrettably, existing approaches struggle to maintain inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion variants. To address this issue, we propose ICON, which improves the inter-report consistency of radiology report generation. Aiming at enhancing the system's ability to capture the similarities in semantically equivalent lesions, our approach involves first extracting lesions from input images and examining their characteristics. Then, we introduce a lesion-aware mix-up augmentation technique to ensure that the representations of the semantically equivalent lesions align with the same attributes, by linearly interpolating them during the training phase. Extensive experiments on three publicly available chest X-ray datasets verify the effectiveness of our approach, both in terms of improving the consistency and accuracy of the generated reports

Probing flavor changing interactions in hadron collisions

The subprocess $gg \to t\bar{c}+\bar{t}c$ in the two-Higgs-doublet model with flavor-changing scalar couplings is examined at the one loop level. With perturbative QCD factorization theorem, the corresponding cross sections for hadron-hadron collisions are computed numerically. The results are applicable to the whole mass range of the weakly coupled Higgs bosons. In case we could efficiently exclude the severe backgrounds of the $t\bar{c}(\bar{t}c)$ production signal, probing the flavor-changing top-charm-scalar vertex at hadron colliders would be very promising and accessible experimentally.Comment: LaTex file, 14 pages, 8 EPS figure

Cloning and selection of reference genes for gene expression studies in Ananas comosus

Full length mRNA sequences of Ac-β-actin and Ac-gapdh, and partial mRNA sequences of Ac-18SrRNA and Ac-ubiquitin were cloned from pineapple in this study. The four genes were tested as housekeeping genes in three experimental sets. GeNorm and NormFinder analysis revealed that β-actin was the most appropriate reference gene for qPCR analysis of callus under induction conditions and in different tissue types, meanwhile, 18SrRNA was the most stable reference gene during organ development. Gapdh was the most unstable gene in all tested experimental sets. Transcript level analysis result of AcSERK1 in stressed callus normalized by β-actin and 18SrRNA further confirmed that reference genes selected in this study were suitable for transcript level analysis of pineapple. The expression pattern of AcSERK1 during somatic embryogenesis normalized by β-actin coincided with the cytological features of calluses during somatic embryogenesis. These results will enable more accurate and reliable normalization of qPCR results for transcription analysis in pineapple. Keywords: Reference genes, qPCR, pineapple, geNorm, NormFinder African Journal of Biotechnology Vol. 11(29), pp. 7424-7433, 10 April, 201

Synthesis of Giant Dendritic Polyphenylenes with 366 and 546 Carbon Atoms and Their High-vacuum Electrospray Deposition

Dendritic polyphenylenes (PPs) can serve as precursors of nanographenes (NGs) if their structures represent 2D projections without overlapping benzene rings. Here, we report the synthesis of two giant dendritic PPs fulfilling this criteria with 366 and 546 carbon atoms by applying a "layer-by-layer" extension strategy. Although our initial attempts on their cyclodehydrogenation toward the corresponding NGs in solution were unsuccessful, we achieved their deposition on metal substrates under ultrahigh vacuum through the electrospray technique. Scanning probe microscopy imaging provides valuable information on the possible thermally induced partial planarization of such giant dendritic PPs on a metal surface

Data-driven optimization of brittleness index for hydraulic fracturing

Evaluation of brittleness index (BI) is a fundamental principle of a hydraulic fracturing design. A wide variety of BI calculations often baffle field engineers. The traditional value comparison may also not make the best of BI. Moreover, it is often mixed up with the fracability in field applications, thus causing concerns. We, therefore, redefine fracability as the fracturing pressure under certain rock mechanical (mainly brittleness), geological and injecting conditions to clarify the confusion. Then, we propose a data-driven workflow to optimize BIs by controlling the geological and injecting conditions. The machine learning (ML) workflow is employed to predict the fracability (fracturing pressure) based on field measurement. Three representative ML algorithms are applied to average the prediction, aiming to restrict the interference of algorithm performances. The contribution of brittleness on pressure/fracability prediction by error analysis (rather than the traditional method of BI-value comparison) is proposed as the new criterion for optimization. Six classic BI correlations (mineral-, logging- and elastic-based) are evaluated, three of which are optimized for the derivation of a new BI using the backward elimination strategy. The stress ratio (ratio of minimum and maximum horizontal principal stress), representing the geological feature, is introduced into the derived calculation based on the independent variable analysis. The reliability of the new BI is verified by error analyses using data of eight fracturing stages from seven different wells. Approximately 40%–50% of the errors are reduced based on the new BI. The differences among the performances of algorithms are also significantly restrained. The new brittleness index provides a more reliable option for evaluating the brittleness and fracability of the fracturing formation. The machine learning workflow also proposes a promising application scenario of the BI for hydraulic fracturing, which makes more efficient and broader usages of the BI compared with the traditional value comparison