[Triterpenoids in Sorbus pohuashanensis suspension cell treated with yeast extract].

This study induced biological stress in Sorbus pohuashanensis suspension cell(SPSC) with yeast extract(YE) as a bio-tic elicitor and isolated and identified secondary metabolites of triterpenoids produced under stress conditions. Twenty-six triterpenoids, including fifteen ursane-type triterpenoids(1-15), two 18,19-seco-ursane-type triterpenoids(16-17), four lupine-type triterpenoids(18-21), two cycloartane-type triterpenoids(22-23), and three squalene-type triterpenoids(24-26), were isolated and purified from the methanol extract of SPSC by chromatography on silica gel, MCI, Sephadex LH-20, and MPLC. Their structures were elucidated by spectroscopic analyses. All triterpenoids were isolated from SPSC for the first time and 22-O-acetyltripterygic acid A(1) was identified as a new compound. Selected compounds were evaluated for antifungal, antitumor, and anti-inflammatory activities, and compound 1 showed an inhibitory effect on NO production in LPS-induced RAW264.7 cells.

A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities.

Early and accurate diagnosis of ear deformities in newborns is crucial for an effective non-surgical correction treatment, since this commonly seen ear anomalies would affect aesthetics and cause mental problems if untreated. It is not easy even for experienced physicians to diagnose the auricular deformities of newborns and the classification of the sub-types, because of the rich bio-metric features embedded in the ear shape. Machine learning has already been introduced to analyze the auricular shape. However, there is little publicly available datasets of ear images from newborns. We released a dataset that contains quality-controlled photos of 3,852 ears from 1,926 newborns. The dataset also contains medical diagnosis of the ear shape, and the health data of each newborn and its mother. Our aim is to provide a freely accessible dataset, which would facilitate researches related with ear anatomies, such as the AI-aided detection and classification of auricular deformities and medical risk analysis.

OW-Adapter: Human-Assisted Open-World Object Detection with a Few Examples.

Open-world object detection (OWOD) is an emerging computer vision problem that involves not only the identification of predefined object classes, like what general object detectors do, but also detects new unknown objects simultaneously. Recently, several end-to-end deep learning models have been proposed to address the OWOD problem. However, these approaches face several challenges: a) significant changes in both network architecture and training procedure are required; b) they are trained from scratch, which can not leverage existing pre-trained general detectors; c) costly annotations for all unknown classes are needed. To overcome these challenges, we present a visual analytic framework called OW-Adapter. It acts as an adaptor to enable pre-trained general object detectors to handle the OWOD problem. Specifically, OW-Adapter is designed to identify, summarize, and annotate unknown examples with minimal human effort. Moreover, we introduce a lightweight classifier to learn newly annotated unknown classes and plug the classifier into pre-trained general detectors to detect unknown objects. We demonstrate the effectiveness of our framework through two case studies of different domains, including common object recognition and autonomous driving. The studies show that a simple yet powerful adaptor can extend the capability of pre-trained general detectors to detect unknown objects and improve the performance on known classes simultaneously.

Long non-coding RNA X-Inactive Specific Transcript (XIST) interacting with USF2 promotes osteogenic differentiation of periodontal ligament stem cells through regulation of WDR72 transcription.

BACKGROUND: Periodontal ligament stem cells (PDLSCs) are the most potential cells in periodontal tissue regeneration and bone tissue regeneration. Our prior work had revealed that WD repeat-containing protein 72 (WDR72) was crucial for osteogenic differentiation of PDLSCs. Here, we further elucidated its underlying mechanism in PDLSC osteogenic differentiation. METHODS: Human PDLSCs, isolated and identified by flow cytometry, were prepared for osteogenic differentiation induction. Levels of WDR72, long non-coding RNA X-Inactive Specific Transcript (XIST), upstream stimulatory factor 2 (USF2), and osteogenic marker genes (Runx2, Osteocalcin, and Collagen I) in human PDLSCs and clinical specimens were detected by RT-qPCR. Protein expressions of WDR72, Runx2, Osteocalcin, and Colla1 were tested by Western blot. The interactions among the molecules were verified by RIP, RNA pull-down, ChIP, and luciferase reporter assays. Osteogenic differentiation was evaluated by alkaline phosphatase (ALP) and alizarin red staining (ARS). RESULTS: WDR72 was decreased in periodontal tissues of periodontitis patients, and overexpression reversed TNF-α-mediated suppressive effects on PDLSC osteogenic differentiation. Mechanically, XIST recruited the enrichment of USF2 to the WDR72 promoter region, thereby positively regulating WDR72. WDR72 silencing overturned XIST-mediated biological effects in PDLSCs. CONCLUSION: WDR72, regulated by the XIST/USF2 axis, enhances osteogenic differentiation of PDLSCs, implying a novel strategy for alleviating periodontitis.

The Effects of BCDs in Unilateral Conductive Hearing Loss: A Systematic Review.

Bone conduction devices (BCDs) are widely used in the treatment of conductive hearing loss (CHL), but their applications on unilateral CHL (UCHL) patients remain controversial. To evaluate the effects of BCDs in UCHL, a systematic search was undertaken until May 2023 following the PRISMA guidelines. Among the 391 references, 21 studies met the inclusion criteria and were ultimately selected for review. Data on hearing thresholds, speech recognition, sound localization, and subjective questionnaire outcomes were collected and summarized. Moderate hearing threshold improvements were found in UCHL patients aided with BCDs. Their speech recognition abilities improved significantly. However, sound localization results showed wide individual variations. According to subjective questionnaires, BCDs had an overall positive influence on the daily life of UCHL patients, although several unfavorable experiences were reported by some of them. We concluded that the positive audiological benefits and subjective questionnaire results have made BCDs a credible intervention for UCHL patients. Before final implantations, UCHL patients should first go through a period of time when they were fitted with non-implantable BCDs as a trial.

SliceTeller: A Data Slice-Driven Approach for Machine Learning Model Validation.

Real-world machine learning applications need to be thoroughly evaluated to meet critical product requirements for model release, to ensure fairness for different groups or individuals, and to achieve a consistent performance in various scenarios. For example, in autonomous driving, an object classification model should achieve high detection rates under different conditions of weather, distance, etc. Similarly, in the financial setting, credit-scoring models must not discriminate against minority groups. These conditions or groups are called as "Data Slices". In product MLOps cycles, product developers must identify such critical data slices and adapt models to mitigate data slice problems. Discovering where models fail, understanding why they fail, and mitigating these problems, are therefore essential tasks in the MLOps life-cycle. In this paper, we present SliceTeller, a novel tool that allows users to debug, compare and improve machine learning models driven by critical data slices. SliceTeller automatically discovers problematic slices in the data, helps the user understand why models fail. More importantly, we present an efficient algorithm, SliceBoosting, to estimate trade-offs when prioritizing the optimization over certain slices. Furthermore, our system empowers model developers to compare and analyze different model versions during model iterations, allowing them to choose the model version best suitable for their applications. We evaluate our system with three use cases, including two real-world use cases of product development, to demonstrate the power of SliceTeller in the debugging and improvement of product-quality ML models.

Three-dimensional finite element analysis on cochlear implantation electrode insertion.

Studying the insertion process of cochlear implant (CI) electrode array (EA) is important to ensure successful, sufficient, and safe implantation. A three-dimensional finite element (FE) model was developed to simulate the insertion process. The cochlear structures were reconstructed from an average statistical shape model (SSM) of human cochlea. The electrode is simplified as a long and tapered beam of homogeneous elastic materials, contacting and interacting with the stiff cochlear structures. A quasi-static insertion simulation was conducted, the insertion force and the contact pressure between the electrode and the cochlear wall, were calculated to evaluate the smoothness of insertion and the risk of potential cochlear trauma. Based on this model, different EA designs were analyzed, including the Young's modulus, the straight or bended shape, the normal or a more tapped section size. The influence of the insertion angle was also discussed. Our simulations indicate that reducing the EA Young's modulus, tapering and pre-bending are effective ways to ensure safe and successful EA implantation. This model is beneficial for optimizing EA designs and is potentially useful for designing patient-specific CI surgery.

Visual Concept Programming: A Visual Analytics Approach to Injecting Human Intelligence at Scale.

Data-centric AI has emerged as a new research area to systematically engineer the data to land AI models for real-world applications. As a core method for data-centric AI, data programming helps experts inject domain knowledge into data and label data at scale using carefully designed labeling functions (e.g., heuristic rules, logistics). Though data programming has shown great success in the NLP domain, it is challenging to program image data because of a) the challenge to describe images using visual vocabulary without human annotations and b) lacking efficient tools for data programming of images. We present Visual Concept Programming, a first-of-its-kind visual analytics approach of using visual concepts to program image data at scale while requiring a few human efforts. Our approach is built upon three unique components. It first uses a self-supervised learning approach to learn visual representation at the pixel level and extract a dictionary of visual concepts from images without using any human annotations. The visual concepts serve as building blocks of labeling functions for experts to inject their domain knowledge. We then design interactive visualizations to explore and understand visual concepts and compose labeling functions with concepts without writing code. Finally, with the composed labeling functions, users can label the image data at scale and use the labeled data to refine the pixel-wise visual representation and concept quality. We evaluate the learned pixel-wise visual representation for the downstream task of semantic segmentation to show the effectiveness and usefulness of our approach. In addition, we demonstrate how our approach tackles real-world problems of image retrieval for autonomous driving.

The effect of bladder function on the efficacy of transurethral prostatectomy in patients with benign prostatic hyperplasia: a retrospective, single-center study / 亚洲男科学杂志(英文版)

We investigated the impact and predictive value of bladder function in patients with benign prostatic hyperplasia (BPH) on the efficacy of transurethral prostatectomy. Symptomatic, imaging, and urodynamic data of patients who underwent transurethral prostatectomy at West China Hospital of Sichuan University (Chengdu, China) from July 2019 to December 2021 were collected. Follow-up data included the quality of life (QoL), International Prostate Symptom Score (IPSS), and IPSS storage and voiding (IPSS-s and IPSS-v). Moreover, urinary creatinine (Cr), nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), and prostaglandin estradiol (PGE2) were measured in 30 patients with BPH and 30 healthy participants. Perioperative indicators were determined by subgroup analyses and receiver operating characteristic (ROC) curve analysis. Among the 313 patients with BPH included, patients with severe micturition problems had more improvements but higher micturition grades postoperatively than those with moderate symptoms. Similarly, good bladder sensation, compliance, and detrusor contractility (DC) were predictors of low postoperative IPSS and QoL. The urinary concentrations of BDNF/Cr, NGF/Cr, and PGE2/Cr in patients were significantly higher than those in healthy participants (all P < 0.001). After evaluation, only DC was significantly related to both urinary indicators and postoperative recovery of patients. Patients with good DC, as predicted by urinary indicators, had lower IPSS and IPSS-v than those with reduced DC at the 1st month postoperatively (both P < 0.05). In summary, patients with impaired bladder function had poor recovery. The combined levels of urinary BDNF/Cr, NGF/Cr, and PGE2/Cr in patients with BPH may be valid predictors of preoperative bladder function and postoperative recovery.

Enhanced recovery after surgery in transurethral surgery for benign prostatic hyperplasia / 亚洲男科学杂志(英文版)

Enhanced recovery after surgery (ERAS) measures have not been systematically applied in transurethral surgery for benign prostatic hyperplasia (BPH). This study was performed on patients with BPH who required surgical intervention. From July 2019 to June 2020, the ERAS program was applied to 248 patients, and the conventional program was applied to 238 patients. After 1 year of follow-up, the differences between the ERAS group and the conventional group were evaluated. The ERAS group had a shorter time of urinary catheterization compared with the conventional group (mean ± standard deviation [s.d.]: 1.0 ± 0.4 days vs 2.7 ± 0.8 days, P < 0.01), and the pain (mean ± s.d.) was significantly reduced through postoperative hospitalization days (PODs) 0-2 (POD 0: 1.7 ± 0.8 vs 2.4 ± 1.0, P < 0.01; POD 1: 1.6 ± 0.9 vs 3.5 ± 1.3, P < 0.01; POD 2: 1.2 ± 0.7 vs 3.0 ± 1.3, P < 0.01). No statistically significant difference was found in the rate of postoperative complications, such as postoperative bleeding (P = 0.79), urinary retention (P = 0.40), fever (P = 0.55), and readmission (P = 0.71). The hospitalization cost of the ERAS group was similar to that of the conventional group (mean ± s.d.: 16 927.8 ± 5808.1 Chinese Yuan [CNY] vs 17 044.1 ± 5830.7 CNY, P =0.85). The International Prostate Symptom Scores (IPSS) and quality of life (QoL) scores in the two groups were also similar when compared at 1 month, 3 months, 6 months, and 12 months after discharge. The ERAS program we conducted was safe, repeatable, and efficient. In conclusion, patients undergoing the ERAS program experienced less postoperative stress than those undergoing the conventional program.

A theoretical framework based on the needs of smart aged care for Chinese community-dwelling older adults: A grounded theory study.

PURPOSE: With technological progress, the integration of aged care with technology is a new challenge. This study developed a theoretical model of smart aged care in the community to meet the diverse needs of community-dwelling older adults. METHODS: This qualitative study recruited 22 participants from three communities in Chongqing, China. Through semi-structured interviews and grounded theory, this study analyzed the needs of community-dwelling older adults for smart aged care and identified strategies. RESULTS: Nine categories were identified, including five need categories, three important factors, and one outcome objective. Furthermore, four health provider topics were proposed. CONCLUSIONS: Although the application of information technology has enhanced convenience and possibilities, its popularity and satisfaction are low. Information technology can be successfully introduced into the lives of community-dwelling older adults only by truly understanding their needs. IMPLICATIONS OF NURSING PRACTICE: Smart aged care in the community has positive effects on nursing outcomes for older adults. This study's findings can help caregivers understand the various dimensions of the needs of community-dwelling older adults and relevant influencing factors under smart aged care to increase its popularity and satisfaction. Furthermore, this can promote the integration of intelligent technology and manual services in nursing practice.

The Effect of Endolymphatic Hydrops and Mannitol Dehydration Treatment on Guinea Pigs.

Background: Endolymphatic hydrops (EH) is considered as the pathological correlate of Menière's disease (MD) and cause of hearing loss. The mechanism of EH, remaining unrevealed, poses challenges for formalized clinical trials. Objective: This study aims to investigate the development of hearing loss, as well as the effect of dehydration treatment on EH animal models. Methods: In this study, different severity EH animal models were created. The laser Doppler vibrometer (LDV) and auditory brainstem responses (ABR) were used to study the effects of EH and the dehydration effects of mannitol. The LDV was used to measure the vibration of the round window membrane (RWM) reflecting the changes in inner ear impedance. ABR was used to evaluate the hearing changes. Furthermore, tissue section and scanning electron microscopy (SEM) observations were used to analyze the anatomical change to the cochlea and outer hair cells. Results: The RWM vibrations decreased with the severity of EH, indicating an increase in the cochlear impedance. The dehydration therapy lowered the impedance to restore acoustic transduction in EH 10- and 20-day animal models. Simultaneously, the ABR thresholds increased in EH models and were restored after dehydration. Moreover, a difference in the hearing was found between ABR and LDV results in severe EH animal models, and the dehydration therapy was less effective, indicating a sensorineural hearing loss (SNHL). Conclusion: Endolymphatic hydrops causes hearing loss by increasing the cochlear impedance in all tested groups, and mannitol dehydration is an effective therapy to restore hearing. However, SNHL occurs for the EH 30-day animal models, limiting the effectiveness of dehydration. Our results suggest the use of dehydrating agents in the early stage of EH.

Impatiensbijieensis (Balsaminaceae), a new species from karst plateau in Guizhou, China.

Impatiensbijieensis X.X. Bai & L.Y. Ren, sp. nov. from northwest Guizhou Province, China, is described and illustrated. This new species is distributed discontinuously in Jiulongshan, Dafang County and Dajiucaiping, Hezhang County, both of which are in the Wumeng Mountain area, a karst plateau landform. The new species is morphologically similar to I.pterosepala Hook.f., I.lasiophyton Hook.f. and I.leptocaulon Hook.f. in height and flower shape and it especially resembles I.lasiophyton in pilosity. However, it differs in its deep purplish-red flower, 2-lobed lower sepal apex and cylindrical capsule. A detailed description, colour photographs and a provisional IUCN Red List assessment are provided along with discussions of its geographical distribution, ecology and morphological relationships with other similar species.

Impatiensliupanshuiensis (Balsaminaceae), a new species from Guizhou, China.

Impatiensliupanshuiensis (Balsamianceae), belonging to I.subgen.Impatiens, is recognised as a new species from Guizhou, China and it is described and illustrated. It is morphologically similar to I.xanthocephala W.W. Sm. in its yellow flowers, extremely small basal lobes on lateral united petals, broadly-dolabriform distal lobes and funnelform lower sepal. However, it is distinctive in the number of lateral sepals, teeth on the margin of lateral sepals, the recurvature of the dorsal petal, the number of lateral veins, the shape and size of the lamina and the type of lamina margin. A detailed description of the new species and colour photographs are provided. Its geographical distribution and morphology are also compared to similar species.

Stability of computed tomography densitometry in patients with otosclerosis:a two-year follow-up.

OBJECTIVES: To quantify the progression of otosclerosis in the unoperated ear between two stapedotomy procedures for patients with bilateral otosclerosis which can help to determine whether a HRCT scan should be re-performed before the second surgery for patients who already received HRCT imaging before the initial surgery. METHODS: 35 patients who underwent bilateral stapedotomy were included. Two rounds of HRCT examination and audiometry were performed at the time of the first surgery and second surgery on the ear that was not operated on during the initial surgery. The relationship between the changes in HRCT densitometry and audiometry over time was analyzed. RESULTS: The second round of HRCT did not add significant information about the changes to the otosclerosis lesions in either the imaging diagnosis or the HRCT density values except for small changes in the HRCT densitometry readings at the area anterior to the inner auditory (P = 0.01). While the changes in HRCT manifestation are small, changes near the fissula ante fenestram (FAF) were still positively correlated with the air bone gap (ABG) of patients (p = 0.031, r = 0.388). CONCLUSIONS: The progression of lesions in otosclerosis is slow resulting in small and insignificant changes to the HRCT features. Therefore, a repeat HRCT evaluations prior to surgery is not necessary for patients who have had a previous HRCT evaluation within 2 years of the operation. The small changes in HRCT manifestation near the FAF were still correlated with negative effects on the ABG which could cause worsened hearing thresholds over this timeframe.

Effects of Various Polymeric Films on the Pericarp Microstructure and Storability of Longan (cv. Shixia) Fruit Treated with Propyl Disulfide Essential Oil from the Neem (Azadirachta indica) Plant.

Plant extracts represent a rich repository of metabolites with antioxidant and antimicrobial properties. Neem (Azadirachta indica) is a medicinal plant considered the tree of the 21st century. In this study, we investigated the antioxidant and antimicrobial effects of propyl disulfide (PD), a major volatile compound in neem seed, against the pericarp browning (BI), microbial decay incidence (DI), and water loss of longan fruit. Fresh longan cv. Shixia samples were packaged in oriented polypropylene (OPP) and polyethene (PE) packages of different thicknesses (20, 40, and 60 µm). Sterile gauze was fixed inside the packages and 500 uL of PD was placed on them to avoid the direct contact of PD with fruit samples. Packages were sealed immediately to minimize vaporization and stored at 12 ± 1 °C for 18 days. Fruit samples packaged in open net packages served as controls. The results showed that fruit treated with PD in OPP and PE packages significantly prevented losses of water, DI, and BI compared to control treatment. PD also maintained the color, TSS values, TA values, pH values, high peel firmness, high TPC content, and high TFC content, and reduced the activity levels of PPO and POD. Scanning electron microscope (SEM) analysis indicated that the exocarp, mesocarp, and endocarp of longan peel were smooth, uniform, and compact with no free space compared to control, where crakes, a damaged and loose structure, and a lot of fungal mycelia were found. The shortest shelf life of 9 days was observed in control as compared to 18 days in OPP-20 and OPP-40; 15 days in OPP-60, PE-20, and PE-40; and 12 days in PE-60 packaging films. Therefore, PD as a natural antioxidant and antimicrobial agent, in combination with OPP-20 and OPP-40 polymeric films, could successfully be applied commercially to extend the postharvest shelf life of longan.

Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models.

The interpretation of deep neural networks (DNNs) has become a key topic as more and more people apply them to solve various problems and making critical decisions. Concept-based explanations have recently become a popular approach for post-hoc interpretation of DNNs. However, identifying human-understandable visual concepts that affect model decisions is a challenging task that is not easily addressed with automatic approaches. We present a novel human-in-the-Ioop approach to generate user-defined concepts for model interpretation and diagnostics. Central to our proposal is the use of active learning, where human knowledge and feedback are combined to train a concept extractor with very little human labeling effort. We integrate this process into an interactive system, ConceptExtract. Through two case studies, we show how our approach helps analyze model behavior and extract human-friendly concepts for different machine learning tasks and datasets and how to use these concepts to understand the predictions, compare model performance and make suggestions for model refinement. Quantitative experiments show that our active learning approach can accurately extract meaningful visual concepts. More importantly, by identifying visual concepts that negatively affect model performance, we develop the corresponding data augmentation strategy that consistently improves model performance.

Interactive Visual Pattern Search on Graph Data via Graph Representation Learning.

Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in images. Identifying subgraph patterns in graphs is an important approach to understand their structural properties. We propose a visual analytics system GraphQ to support human-in-the-loop, example-based, subgraph pattern search in a database containing many individual graphs. To support fast, interactive queries, we use graph neural networks (GNNs) to encode a graph as fixed-length latent vector representation, and perform subgraph matching in the latent space. Due to the complexity of the problem, it is still difficult to obtain accurate one-to-one node correspondences in the matching results that are crucial for visualization and interpretation. We, therefore, propose a novel GNN for node-alignment called NeuroAlign, to facilitate easy validation and interpretation of the query results. GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints. We demonstrate GraphQ through two example usage scenarios: analyzing reusable subroutines in program workflows and semantic scene graph search in images. Quantitative experiments show that NeuroAlign achieves 19%-29% improvement in node-alignment accuracy compared to baseline GNN and provides up to 100× speedup compared to combinatorial algorithms. Our qualitative study with domain experts confirms the effectiveness for both usage scenarios.

Where Can We Help? A Visual Analytics Approach to Diagnosing and Improving Semantic Segmentation of Movable Objects.

Semantic segmentation is a critical component in autonomous driving and has to be thoroughly evaluated due to safety concerns. Deep neural network (DNN) based semantic segmentation models are widely used in autonomous driving. However, it is challenging to evaluate DNN-based models due to their black-box-like nature, and it is even more difficult to assess model performance for crucial objects, such as lost cargos and pedestrians, in autonomous driving applications. In this work, we propose VASS, a Visual Analytics approach to diagnosing and improving the accuracy and robustness of Semantic Segmentation models, especially for critical objects moving in various driving scenes. The key component of our approach is a context-aware spatial representation learning that extracts important spatial information of objects, such as position, size, and aspect ratio, with respect to given scene contexts. Based on this spatial representation, we first use it to create visual summarization to analyze models' performance. We then use it to guide the generation of adversarial examples to evaluate models' spatial robustness and obtain actionable insights. We demonstrate the effectiveness of VASS via two case studies of lost cargo detection and pedestrian detection in autonomous driving. For both cases, we show quantitative evaluation on the improvement of models' performance with actionable insights obtained from VASS.