Short-term steaming during processing impacts the quality of Citri Reticulatae 'Chachi' peel.

Citrus peel is a commonly used food-medicine material in the production of fast-moving consumer goods (FMCGs). For instance, Ganpu tea is manufactured by combining the peel of Citri Reticulatae 'Chachi' (PCRC) with Pu-erh tea. The alleviated irritation of PCRC through years of aging makes Citri reticulatae Pericarpium a traditional Chinese medicine. Herein, we introduced short-term steaming into the processing of PCRC to favor the quick removal of its irritation while retaining its food-medicine properties. Sensory evaluation and volatile component analysis showed that 60-s steaming reduced irritation of freshly prepared PCRC. Biological evaluations indicated no effects of steaming on the neuroprotective activity of PCRC. The process increased the contents of several bioactive ingredients, including hesperidin, nobiletin, tangeretin, and synephrine. In addition, physical indications of accelerating PCRC aging were observed. Taken together, our findings suggest that short-term steaming may offer a promising new possibility for enhancing the quality of citrus peel.

Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation.

Visual Question Answering on 3D Point Cloud (VQA-3D) is an emerging yet challenging field that aims at answering various types of textual questions given an entire point cloud scene. To tackle this problem, we propose the CLEVR3D, a large-scale VQA-3D dataset consisting of 171K questions from 8,771 3D scenes. Specifically, we develop a question engine leveraging 3D scene graph structures to generate diverse reasoning questions, covering the questions of objects' attributes (i.e., size, color, and material) and their spatial relationships. Through such a manner, we initially generated 44K questions from 1,333 real-world scenes. Moreover, a more challenging setup is proposed to remove the confounding bias and adjust the context from a common-sense layout. Such a setup requires the network to achieve comprehensive visual understanding when the 3D scene is different from the general co-occurrence context (e.g., chairs always exist with tables). To this end, we further introduce the compositional scene manipulation strategy and generate 127K questions from 7,438 augmented 3D scenes, which can improve VQA-3D models for real-world comprehension. Built upon the proposed dataset, we baseline several VQA-3D models, where experimental results verify that the CLEVR3D can significantly boost other 3D scene understanding tasks. Our code and dataset are publicly available at https://github.com/yanx27/CLEVR3D.

First Report of Sclerotinia sclerotiorum Causing Hibiscus rosa-sinensis Stem Rot in Guangxi, China.

Hibiscus rosa-sinensis, native to the south of China, is currently planted as an important landscaping tree species in more than 100 countries around the world. Since 2012, an unknown stem rot disease of H. rosa-sinensis has occurred sporadically in a few green belts of Nanning, Guangxi, China. In February 2023, the incidence rate of the disease in the southern part of the city (108°38'E, 22°77'N) reached 5-8%. The pathogen mainly infected the stems near the soil line and aboveground stems. Initially, brown spots appeared and developed into long strips of large spots around the stem, slightly sunken. Later, the diseased tissue cortex presented longitudinal cracks and the vascular bundle tissue was exposed like silk hemp. White mycelium appeared on the diseased stem surfaces under high humidity conditions, eventually maturing into hard black sclerotia (1.5 to 11.0 mm in length). The leaves turned yellow and the whole plant finally died. For fungal isolation, seven diseased plants distributed within 800 square meters were collected, and 35 symptomatic stem sections were surface disinfect with alcohol for 30s, 0.08% NaClO for 1 min, triple rinsed with sterile distilled water, and cultured in potato dextrose agar (PDA) medium at 28℃. Sclerotinia-like colonies were consistently isolated from all diseased tissues and four isolates (Z1-Z4) were purified (Bolton et al. 2006). Irregular white immature sclerotia were produced after 5 to 7 days on the edges of the plates and turned black after 7 to 14 days, with a size of 1.8 to 4.6 × 1.2 to 3.4 mm (avg. 3.3 × 2.4 mm, n = 20). For molecular characterization, three gene regions (ITS, CaM and Mcm7) were amplified (White et al. 1990; Carbone et al. 1999; Schmitt et al. 2009) and sequenced (GenBank accession nos.: ITS: OR016764 to OR016767; CaM: OR257811 to OR257814; Mcm7: OR345318 to OR345321). The sequences of three analyzed DNA fragments shared 100% identity with sequences of Sclerotinia sclerotiorum strains (accession nos. JN013184, AF341304, KF545468). To fullfill Koch's postulates, healthy H. rosa-sinensis nursery stocks at the six months stage were individually planted in plastic pots at 25±3℃. The base of the stem and upper three branches of each plant were wounded with a sterile needle and inoculated with 5-mm discs of mycelium grown on PDA, then the inoculation sites of stem bases were covered with one layer nursery substrate and those of branches were wrapped with transparent tape to maintain the humidity. Three plants were inoculated with each isolate. As a control, three plants were inoculated with PDA discs. All the inoculated plants with mycelial discs developed characteristic symptoms 5 to 8 days after inoculation. The inoculation sites appeared white mycelium and the leaves sagged and wilted. Later, black sclerotia appeared on the diseased stem and the whole plant withered, while the control plants remained symptomless. Fungal cultures reisolated from symptomatic plants were morphologically identical with the cultures used as inoculum. Sclerotinia sclerotiorum has only been reported from H. rosa-sinensis in Taiwan (Tai 1979). The pathogen is a widely distributed fungus, causing many economically important diseases on various plants (Hossain et al. 2023). To our knowledge, this is the first report of S. sclerotiorum causing H. rosa-sinensis stem rot in Chinese Mainland, laying the foundation for monitoring its occurrence and spread.

Spatio-Temporal Contextual Learning for Single Object Tracking on Point Clouds.

Single object tracking (SOT) is one of the most active research directions in the field of computer vision. Compared with the 2-D image-based SOT which has already been well-studied, SOT on 3-D point clouds is a relatively emerging research field. In this article, a novel approach, namely, the contextual-aware tracker (CAT), is investigated to achieve a superior 3-D SOT through spatially and temporally contextual learning from the LiDAR sequence. More precisely, in contrast to the previous 3-D SOT methods merely exploiting point clouds in the target bounding box as the template, CAT generates templates by adaptively including the surroundings outside the target box to use available ambient cues. This template generation strategy is more effective and rational than the previous area-fixed one, especially when the object has only a small number of points. Moreover, it is deduced that LiDAR point clouds in 3-D scenes are often incomplete and significantly vary from frame to another, which makes the learning process more difficult. To this end, a novel cross-frame aggregation (CFA) module is proposed to enhance the feature representation of the template by aggregating the features from a historical reference frame. Leveraging such schemes enables CAT to achieve a robust performance, even in the case of extremely sparse point clouds. The experiments confirm that the proposed CAT outperforms the state-of-the-art methods on both the KITTI and NuScenes benchmarks, achieving 3.9% and 5.6% improvements in terms of precision.

Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning.

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.

Serum exosomal miR-122-5p is a new biomarker for both acute coronary syndrome and underlying coronary artery stenosis.

PURPOSE: Acute coronary syndrome presents as unstable angina (UA) or acute myocardial infarction (AMI). We explored the use of exosomal miR-122-5p as a biomarker for UA and AMI and determined whether its expression level is positively correlated with the severity of coronary stenosis. METHODS: This study enrolled 34 patients with AMI, 31 patients with UA, and 22 control subjects. qPCR was used to detect the expression levels of serum exosomal miR-122-5p. RESULTS: The expression of serum exosomal miR-122-5p in UA and AMI patients was significantly higher than that in the control group, and expression levels differed between UA and AMI patients. Receiver operating characteristic analysis demonstrated that serum exosomal miR-122-5p might be used as a diagnostic biomarker for AMI and UA. In addition, we also found that serum exosomal miR-122-5p was positively correlated with the severity of coronary artery stenosis for UA patients based on the Gensini score. Serum exosomal miR-122-5p was highly expressed in patients with a coronary artery stenosis severity greater than 80% during acute coronary syndrome. CONCLUSION: Serum exosomal miR-122-5p might be useful as a diagnostic biomarker for AMI and UA, and increased serum exosomal miR-122-5p levels could be useful to predict the severity of coronary lesions.