Enhancing Recommendation Interpretability with Tags: A Neural Variational Model

Recommender systems are widely used for assisting consumers finding interested products, and providing suitable explanations for recommendation is particularly important for enhancing consumers’ trust and satisfaction with the system. Tags can be used to annotate different types of items, yet their potential for providing interpretability is not well studied previously. Therefore, it is worthy to study how to leverage tags to enhance recommendation systems in terms of both interpretability and accuracy. This paper proposes a novel model that seamlessly fuse topic model and recommendation model, where the topic model can analyze tags to infer understandable topics, and the recommendation model can conduct accurate and interpretable recommendations based on these topics. We develop variational auto-encoding method to take advantage of neural networks to infer model parameters. Experiments on real-world datasets illustrate that the proposed method can not only achieve great recommendation performance, but also provide interpretability for the recommendation results

Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis

A time-frequency analysis method based on improved variational mode decomposition and Teager energy operator (IVMD-TEO) is proposed for fault diagnosis of turbine rotor. Variational mode decomposition (VMD) can decompose a multicomponent signal into a number of band-limited monocomponent signals and can effectively avoid model mixing. To determine the number of monocomponents adaptively, VMD is improved using the correlation coefficient criterion. Firstly, IVMD algorithm is used to decompose a multicomponent signal into a number of monocompositions adaptively. Second, all the monocomponent signals’ instantaneous amplitude and instantaneous frequency are demodulated via TEO, respectively, because TEO has fast and high precision demodulation advantages to monocomponent signal. Finally, the time-frequency representation of original signal is exhibited by superposing the time-frequency representations of all the monocomponents. The analysis results of simulation signal and experimental turbine rotor in rising speed condition demonstrate that the proposed method has perfect multicomponent signal decomposition capacity and good time-frequency expression. It is a promising time-frequency analysis method for rotor fault diagnosis

Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study

PurposeThe purpose of this study was to distinguish pneumonic-type mucinous adenocarcinoma (PTMA) from lobar pneumonia (LP) by pre-treatment CT radiological and clinical or radiological parameters.MethodsA total of 199 patients (patients diagnosed with LP = 138, patients diagnosed with PTMA = 61) were retrospectively evaluated and assigned to either the training cohort (n = 140) or the validation cohort (n = 59). Radiomics features were extracted from chest CT plain images. Multivariate logistic regression analysis was conducted to develop a radiomics model and a nomogram model, and their clinical utility was assessed. The performance of the constructed models was assessed with the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The clinical application value of the models was comprehensively evaluated using decision curve analysis (DCA).ResultsThe radiomics signature, consisting of 14 selected radiomics features, showed excellent performance in distinguishing between PTMA and LP, with an AUC of 0.90 (95% CI, 0.83–0.96) in the training cohort and 0.88 (95% CI, 0.79–0.97) in the validation cohort. A nomogram model was developed based on the radiomics signature and clinical features. It had a powerful discriminative ability, with the highest AUC values of 0.94 (95% CI, 0.90–0.98) and 0.91 (95% CI, 0.84–0.99) in the training cohort and validation cohort, respectively, which were significantly superior to the clinical model alone. There were no significant differences in calibration curves from Hosmer–Lemeshow tests between training and validation cohorts (p = 0.183 and p = 0.218), which indicated the good performance of the nomogram model. DCA indicated that the nomogram model exhibited better performance than the clinical model.ConclusionsThe nomogram model based on radiomics signatures of CT images and clinical risk factors could help to differentiate PTMA from LP, which can provide appropriate therapy decision support for clinicians, especially in situations where differential diagnosis is difficult

The efficacy and safety analysis of first-line immune checkpoint inhibitors in pulmonary sarcomatoid carcinoma

BackgroundPulmonary sarcomatoid carcinoma (PSC) is a rare and aggressive disease without standardized treatment strategies. The efficacy of second-line or beyond immune checkpoint inhibitors (ICIs) has been proven in recent studies, whereas the evidence for first-line immunotherapy for PSC is still limited to case reports and remains poorly understood.Materials and methodsThis was a multicenter, retrospective analysis of 21 patients with a histological diagnosis of PSC who received ICI as first-line therapy from January 2019 to March 2022. The expression of PD-L1 was evaluated by immunohistochemistry (IHC) using the monoclonal antibody 22C3. Low and high PD-L1 expressions were defined using the tumor proportion score (TPS), with cutoffs of 1 and 50%, respectively.ResultsAll eight patients had PD-L1 positivity who underwent PD-L1 expression assessment, and six patients (6/8, 75.0%) had high PD-L1 expression. Among the 21 PSC patients, seven received tislelizumab, six received camrelizumab, four received sintilimab, three received pembrolizumab, and one received durvalumab. Among them, 18 PSCs received combination therapy, whereas another three PSCs received immunotherapy alone. Out of the 21 PSC patients, 12 (57.1%) achieved a partial response (PR), and five patients had stable disease (SD) as the best response, whereas four PSCs experienced dramatic progressive disease (PD). The median progression-free survival (PFS) was 9.2 (95% CI [4.3, 14.1]) months, and the median OS was 22.8 (95% CI [4.0, 41.5]) months. Among the three treatment groups (immunotherapy alone, immunotherapy combined with anlotinib, and chemoimmunotherapy), the median PFS was 8.0, 9.4, and 9.6 months, and the median OS was 19.0, 22.8, and 30.6 months, respectively. There was no difference in PFS and OS between the three treatment regimen groups (P = 0.86 and P = 0.34, respectively) and different immunotherapies (P = 0.10 and P = 0.23, respectively). No serious adverse events (grade ≥ 3) were noted.ConclusionFirst-line immunotherapy has promising therapeutic potential in the treatment of PSC. More studies are warranted to confirm these findings

Tissue engineered corneal epithelium derived from clinical-grade human embryonic stem cells

角膜上皮位于角膜最表层，对维持角膜的透明性发挥非常重要的作用。角膜上皮细胞处于不断更新之中，它们更新的源泉在于角膜缘处的上皮干细胞，即角膜缘干细胞。眼表面化学伤、热烧伤以及部分先天性遗传疾病可以引起角膜缘干细胞缺乏，或者使干细胞丧失向正常角膜上皮细胞分化的能力，导致经久不愈的角膜上皮缺损、角膜新生血管、角膜上皮结膜化、角膜溃疡等病理改变，严重影响患者视力，甚至导致失明。由于眼表面的严重破坏，这类患者的治疗具有很大的挑战性，目前比较理想的治疗方法是进行组织工程角膜上皮移植。 厦门大学眼科研究所和中国科学院动物研究所干细胞与生殖生物学国家重点实验室合作研究采用中科院动物研究所研发的我国第一个临床级胚胎干细胞系Q-CTS-hESC-1，体外诱导分化为角膜上皮祖细胞，在此基础上构建组织工程角膜上皮，移植于兔角膜缘干细胞缺乏动物模型，成功重建眼表面。 厦门大学眼科研究所博士研究生贺佳和欧尚坤为该论文的共同第一作者，厦门大学眼科研究所李炜教授、刘祖国教授以及中科院动物所郝捷副研究员为共同通讯作者。【Abstract】Purpose: To construct tissue engineered corneal epithelium from a clinical-grade human embryonic stem cells (hESCs) and investigate the dynamic gene profile and phenotypic transition in the process of differentiation.Methods: A stepwise protocol was applied to induce differentiation of clinical-grade hESCs Q-CTS-hESC-1 and construct tissue engineered corneal epithelium. Single cell RNA sequencing (scRNA-seq) analysis was performed to monitor gene expression and phenotypic changes at different differentiation stages. Immunostaining, realtime quantitative PCR and Western blot analysis were conducted to detect gene and protein expressions. After subcutaneous transplantation into nude mice to test the biosafety, the epithelial construct was transplanted in a rabbit corneal limbal stem cell deficiency (LSCD) model and followed up for eight weeks. Results: The hESCs were successfully induced into epithelial cells. scRNA-seq analysis revealed upregulation of ocular surface epithelial cell lineage related genes such as TP63, Pax6, KRT14, and activation of Wnt, Notch,Hippo, and Hedgehog signaling pathways during the differentiation process. Tissue engineered epithelial cell sheet derived from hESCs showed stratified structure and normal corneal epithelial phenotype with presence of clonogenic progenitor cells. Eight weeks after grafting the cell sheet onto the ocular surface of LSCD rabbit model, a full-thickness continuous corneal epithelium developed to fully cover the damaged areas with normal limbal and corneal epithelial phenotype. Conclusion: The tissue engineered corneal epithelium generated from a clinical-grade hESCs may be feasible in the treatment of limbal stem cell deficiency.This study was supported in part by the National Key R&D Program of China (2018YFA0107301 [to WL], 2018YFA0107304 [to ZL], 2013CB967003 [to WL]), the National Natural Science Foundation of China (NSFC, No.81770894, No.81470602 [to WL], and No.81330022 [to ZL]). 该论文获得了国家重点研发计划项目和国家自然科学基金项目的资助

Automatic Tracking of Muscle Fiber Direction in Ultrasound Images Based on Improved Kalman Filter

Ultrasound myograph (SMG) is a real-time and dynamic acquisition of muscle structure parameter changes by recording ultrasound images of muscle contraction through an ultrasound instrument. Muscle parameters are essential for judging whether the muscle and the human body are healthy. In order to solve the problem of muscle fiber tracking in a sequence of ultrasound muscle images, we propose a method to track the direction of muscle fibers automatically based on the improved Kalman filter. Firstly, the measurement value of the muscle fiber direction is obtained by introducing a reference line into the ultrasound muscle image based on deep learning. Secondly, the framework of a Kalman filter is improved by introducing a set of neural units. Finally, the optimal estimated value of muscle fiber direction is obtained by combining the measured value with the improved Kalman filter. It is verified by conducting experiments where the result obtained by our proposed method is closer to the manually labeled value compared with the original measurement method, and the root mean square error is reduced by about 10%