In immunology studies, flow cytometry is a commonly used multivariate single-cell assay. One key goal in flow cytometry analysis is to detect the immune cells responsive to certain stimuli. Statistically, this problem can be translated into comparing two protein expression probability density functions (pdfs) before and after the stimulus; the goal is to pinpoint the regions where these two pdfs differ. Further screening of these differential regions can be performed to identify enriched sets of responsive cells. In this paper, we model identifying differential density regions as a multiple testing problem. First, we partition the sample space into small bins. In each bin, we form a hypothesis to test the existence of differential pdfs. Second, we develop a novel multiple testing method, called TEAM (Testing on the Aggregation tree Method), to identify those bins that harbor differential pdfs while controlling the false discovery rate (FDR) under the desired level. TEAM embeds the testing procedure into an aggregation tree to test from fine- to coarse-resolution. The procedure achieves the statistical goal of pinpointing density differences to the smallest possible regions. TEAM is computationally efficient, capable of analyzing large flow cytometry data sets in much shorter time compared with competing methods. We applied TEAM and competing methods on a flow cytometry data set to identify T cells responsive to the cytomegalovirus (CMV)-pp65 antigen stimulation. With additional downstream screening, TEAM successfully identified enriched sets containing monofunctional, bifunctional, and polyfunctional T cells. Competing methods either did not finish in a reasonable time frame or provided less interpretable results. Numerical simulations and theoretical justifications demonstrate that TEAM has asymptotically valid, powerful, and robust performance. Overall, TEAM is a computationally efficient and statistically powerful algorithm that can yield meaningful biological insights in flow cytometry studies.

Gardeniae Fructus (Zhizi) serves as both a medicinal and edible substance and finds widespread use in various industries. There are often two kinds of medicinal materials in the market: Zhizi and Shuizhizi. Typically, Zhizi with small, round fruit is used for medicinal purposes, while Shuizhizi, characterized by large, elongated fruit, is employed for dyeing. Market surveys have revealed a diverse range of Zhizi types, and modern research indicates that Shuizhizi contains rich chemical components and pharmacological activities. In this study, we collected 25 batches of Zhizi and Shuizhizi samples, categorizing them based on appearance into obovate and round fruits, with seven length grades (A-G). Using the ultra-high performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-QQQ-MS/MS) method, we simultaneously quantified 13 main chemical components in fruits of Gardenia species. In addition, we compared the weight percentage of the pericarp, flesh, and seeds parts of samples with different traits, and quantified 13 chemical components in different parts. Results indicated that, aside from a few instances of overlapping fruit size ranges, Shuizhizi generally exhibits larger and longer dimensions than Zhizi. The weight proportion of the Shuizhizi pericarp is often higher than that of the Zhizi pericarp. Quantitative results highlighted significant differences in the chemical component content between Zhizi and Shuizhizi, with Shuizhizi generally containing higher levels of iridoids. The PCA and OPLS-DA analysis distinctly divided Shuizhizi and Zhizi, among which three iridoids, two organic acids, and one flavonoid made significant contributions to their classification. Cluster heatmap analysis also demonstrated complete separation between Zhizi and Shuizhizi, with clear distinctions among Zhizi samples from different origins. The distribution of the 13 chemical components in different Zhizi and Shuizhizi parts remained consistent, with iridoids and pigments concentrated in the seeds and flesh, and two organic acids and one flavonoid enriched in the pericarp. In summary, this study contributes valuable insights for classifying Zhizi and offers guidance on the rational use of Shuizhizi and the different parts of Zhizi.

Recently, due to the difficulty of collecting condition data covering all mechanical fault types in industrial scenarios, the fault diagnosis problem under incomplete data is receiving increasing attention where no target prior information can be available. The existing open-set or universal domain adaptation (DA) diagnosis methods typically treat private fault samples in the target as a generalized \"unknown\" fault class, neglecting their inherent structure. This oversight can lead to confusion in latent feature space representations and difficulties in separating unknown samples. Therefore, a universal DA method with unsupervised clustering is developed to explore the intrinsic structure of the target samples for mechanical fault diagnosis, where multi-source information on different working conditions is considered to transfer complementary knowledge. First, a composite clustering metric combining single-domain and cross-domain evaluation is constructed to recognize shared and unknown health classes on source-target domains. Second, to alleviate the intra-class shift while enlarging the inter-class gap, a class-wise DA algorithm is suggested which operates on the basis of maximum mean discrepancy. Finally, an entropy regularization criterion is utilized to facilitate clustering of different health classes. The efficacy of the presented approach in the fault diagnosis issues when monitoring data is inadequate has been verified through extensive experiments on three rotating machinery datasets.

The objectives of this study were to identify both legacy and emerging per- and polyfluoroalkyl substances (PFAS) from three typical fluoridated industrial parks (FIPs) in China, and to assess their environmental occurrence and fate. Complementary suspect target and nontarget screening were implemented, and a total of 111 emerging PFAS were identified. Based on the multi-mass scale analysis, 25 emerging PFAS were identified for the first time, including 24 per- and polyfluoroalkyl ether carboxylic acids (PFECAs) and 1 ultra-short chlorinated perfluoroalkyl carboxylic acids (Cl-PFCAs, C2), with a maximum percentage of 48.2 % in nontarget PFAS (exclude target PFAS). The composition of PFAS identified in different media was influenced by functional groups, carbon chain length, substituents and ether bond insertion, with poly-hydrogen substituted being preferably in water and a more diverse pattern of PFECAs in sediments. The patterns of PFAS homologs revealed distinct differences among the three typical FIPs in the shift of PFAS production patterns. The C4-PFAS and short-chain carboxylic acids (≤C6) were the main PFAS in the Fuxin and Changshu, respectively. In contrast, perfluorooctanoic acid (PFOA, C8) remained dominant in Zibo, and the highest point concentrations in water and sediment were up to 706 µg/L and 553 µg/g, respectively.

Intelligent fault diagnosis aims to build robust mechanical condition recognition models with limited dataset. At this stage, fault diagnosis faces two practical challenges: (1) the variability of mechanical working conditions makes the collected data distribution inconsistent, which brings about the domain shift; (2) some unpredictable unknown fault modes that do not observe in the training dataset may occur in the testing scenario, leading to a category gap. In order to cope with these two entangled challenges, an open set multi-source domain adaptation approach is developed in this study. Specifically, a complementary transferability metric defined on multiple classifiers is introduced to quantify the similarity of each target sample to known classes to weight the adversarial mechanism. By applying an unknown mode detector, unknown faults can be automatically identified. Moreover, a multi-source mutual-supervised strategy is further adopted to mine relevant information between different sources to enhance the model performance. Extensive experiments are conducted on three rotating machinery datasets, and the results show that the proposed method is superior to traditional domain adaptation approaches in the mechanical diagnosis issues that new fault modes occur.

The multi-process manufacturing of steel rolling products requires the cooperation of complicated and variable rolling conditions. Such conditions pose challenges to the fault diagnosis of the key equipment of the rolling mill. The development of transfer learning has alleviated the problem of fault diagnosis under variable working conditions to a certain extent. However, existing diagnosis methods based on transfer learning only consider the distribution alignment from a single representation, which may only transfer part of the state knowledge and generate fuzzy decision boundaries. Therefore, this paper proposes a multi-representation domain adaptation network with duplex adversarial learning for hot rolling mill fault diagnosis. First, a multi-representation network structure is designed to extract rolling mill equipment status information from multiple perspectives. Then, the domain adversarial strategy is adopted to match the source and target domains of each pair of representations for learning domain-invariant features from multiple representation networks. In addition, the maximum classifier discrepancy adversarial algorithm is adopted to generate target features that are close to the source support, thereby forming a robust decision boundary. Finally, the average value of the predicted probabilities of the two classifiers is used as the final diagnostic result. Extensive experiments are conducted on an experimental platform of a four-high hot rolling mill to collect the fault state data of the reduction gearbox and roll bearing. The experimental results reveal that the method can effectively realize the fault diagnosis of rolling mill equipment under variable working conditions and can achieve average diagnostic rates of up to 99.15% and 99.40% on the data sets of the rolling mill gearbox and bearing, which are respectively 2.19% and 1.93% higher than the rates achieved by the most competitive method.

As a critical variable in the roasting process, the roasting temperature has a significant influence on operating conditions. Model predictive control (MPC) provides a path to stabilize the roasting temperature. However, process data collected at different periods usually follow different distributions due to the fluctuation of feed composition for the roasting process, result in a model mismatch on online control. For this reason, a transfer predictive control method based on inter-domain mapping learning (IDML-MPC) is proposed. The proposed method first treat historical and online data as two domains. Then, a distribution mapping function from one domain to another domain is learned to make the distribution of the historical data follow that of the online data. Finally, an accurate online prediction model is built, roasting temperature control is achieved by minimizing the cost function with respect to the predicted value and the control input. The effectiveness of the proposed method is demonstrated by comparative experiments based on a numerical example and a simulation platform of the roasting process. Experimental results compared with some state-of-the-art methods show that it is necessary to take into account the distribution differences between historical data and online data when production conditions change. The IDML-MPC improved the control performance for the roasting temperature with an average 56.98% reduction in the root mean square error.