dwMLCS: An Efficient MLCS Algorithm based on Dynamic and Weighted Directed Acyclic Graph.

The problem of finding the longest common subsequence (MLCS) for multiple sequences is a computationally intensive and challenging problem that has significant applications in various fields such as text comparison, pattern recognition, and gene diagnosis. Currently, the dominant point-based MLCS algorithms have become popular and extensively studied. Generally, they construct the directed acyclic graph (DAG) of matching points and convert the MLCS problem into a search for the longest paths in the DAG. Several improvements have been made, focusing on decreasing model size and reducing redundant computations. These include 1) hash methods for eliminating duplicated nodes, 2) dynamic structures for supporting smaller DAG and 3) path pruning strategy and so on. However, the algorithms are still too limited when facing large-scale MLCS problem due to 1) the dynamic structures are too time-consuming to maintain and 2) the path pruning relies heavily on the tightness of the lower and upper bound of the MLCS. These factors contribute to the large-scale MLCS problem remaining a challenge. We propose a novel algorithm for the large-scale MLCS problem, named dwMLCS. It is based on two models: one is a dynamic DAG model which is both space and time efficient. It can decrease the size of the DAG significantly. The other is a weighted DAG model with new successor strategies. With this model, we design the algorithm for finding a tighter lower bound of the MLCS. Then, the path pruning is conducted to further reduce the size of the DAG and eliminate redundant computation. Additionally, we propose an upper bound method for improving the efficiency of the path pruning strategy. The experimental results demonstrate that the effectiveness and efficiency of the models and algorithms proposed are better than state-of-the-art algorithms. The source codes of dwMLCS can be downloaded from web site https://github.com/BioLab310/dwMLCS.

InsigHTable: Insight-driven Hierarchical Table Visualization with Reinforcement Learning.

Embedding visual representations within original hierarchical tables can mitigate additional cognitive load stemming from the division of users' attention. The created hierarchical table visualizations can help users understand and explore complex data with multi-level attributes. However, because of many options available for transforming hierarchical tables and selecting subsets for embedding, the design space of hierarchical table visualizations becomes vast, and the construction process turns out to be tedious, hindering users from constructing hierarchical table visualizations with many data insights efficiently. We propose InsigHTable, a mixed-initiative and insight-driven hierarchical table transformation and visualization system. We first define data insights within hierarchical tables, which consider the hierarchical structure in the table headers. Since hierarchical table visualization construction is a sequential decision-making process, InsigHTable integrates a deep reinforcement learning framework incorporating an auxiliary rewards mechanism. This mechanism addresses the challenge of sparse rewards in constructing hierarchical table visualizations. Within the deep reinforcement learning framework, the agent continuously optimizes its decision-making process to create hierarchical table visualizations to uncover more insights by collaborating with analysts. We demonstrate the usability and effectiveness of InsigHTable through two case studies and sets of experiments. The results validate the effectiveness of the deep reinforcement learning framework and show that InsigHTable can facilitate users to construct hierarchical table visualizations and understand underlying data insights.

MiniDBG: A Novel and Minimal De Bruijn Graph for Read Mapping.

The De Bruijn graph (DBG) has been widely used in the algorithms for indexing or organizing read and reference sequences in bioinformatics. However, a DBG model that can locate each node, edge and path on sequence has not been proposed so far. Recently, DBG has been used for representing reference sequences in read mapping tasks. In this process, it is not a one-to-one correspondence between the paths of DBG and the substrings of reference sequence. This results in the false path on DBG, which means no substrings of reference producing the path. Moreover, if a candidate path of a read is true, we need to locate it and verify the candidate on sequence. To solve these problems, we proposed a DBG model, called MiniDBG, which stores the position lists of a minimal set of edges. With the position lists, MiniDBG can locate any node, edge and path efficiently. We also proposed algorithms for generating MiniDBG based on an original DBG and algorithms for locating edges or paths on sequence. We designed and ran experiments on real datasets for comparing them with BWT-based and position list-based methods. The experimental results show that MiniDBG can locate the edges and paths efficiently with lower memory costs.

SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation.

Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source domain. One popular solution is self-training, which retrains the model with pseudo labels on target instances. Plenty of approaches tend to alleviate noisy pseudo labels, however, they ignore the intrinsic connection of the training data, i.e., intra-class compactness and inter-class dispersion between pixel representations across and within domains. In consequence, they struggle to handle cross-domain semantic variations and fail to build a well-structured embedding space, leading to less discrimination and poor generalization. In this work, we propose emantic-Guided Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights the semantic concepts of individual pixels to promote learning of class-discriminative and class-balanced pixel representations across domains, eventually boosting the performance of self-training methods. Specifically, to explore proper semantic concepts, we first investigate a centroid-aware pixel contrast that employs the category centroids of the entire source domain or a single source image to guide the learning of discriminative features. Considering the possible lack of category diversity in semantic concepts, we then blaze a trail of distributional perspective to involve a sufficient quantity of instances, namely distribution-aware pixel contrast, in which we approximate the true distribution of each semantic category from the statistics of labeled source data. Moreover, such an optimization objective can derive a closed-form upper bound by implicitly involving an infinite number of (dis)similar pairs, making it computationally efficient. Extensive experiments show that SePiCo not only helps stabilize training but also yields discriminative representations, making significant progress on both synthetic-to-real and daytime-to-nighttime adaptation scenarios. The code and models are available at https://github.com/BIT-DA/SePiCo.

An efficient privacy-preserving blockchain storage method for internet of things environment.

Blockchain is a key technology to realize decentralized trust management. In recent studies, sharding-based blockchain models are proposed and applied to the resource-constrained Internet of Things (IoT) scenario, and machine learning-based models are presented to improve the query efficiency of the sharding-based blockchains by classifying hot data and storing them locally. However, in some scenarios, these presented blockchain models cannot be deployed because the block features used as input in the learning method are privacy. In this paper, we propose an efficient privacy-preserving blockchain storage method for the IoT environment. The new method classifies hot blocks based on the federated extreme learning machine method and saves the hot blocks through one of the sharded blockchain models called ElasticChain. The features of hot blocks will not be read by other nodes in this method, and user privacy is effectively protected. Meanwhile, hot blocks are saved locally, and data query speed is improved. Furthermore, in order to comprehensively evaluate a hot block, five features of hot blocks are defined, including objective feature, historical popularity, potential popularity, storage requirements and training value. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the proposed blockchain storage model.

Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering.

In this paper, we explore the problem of deep multi-view subspace clustering framework from an information-theoretic point of view. We extend the traditional information bottleneck principle to learn common information among different views in a self-supervised manner, and accordingly establish a new framework called Self-supervised Information Bottleneck based Multi-view Subspace Clustering (SIB-MSC). Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views. Actually, the latent representation of each view provides a kind of self-supervised signal for training the latent representations of other views. Moreover, SIB-MSC attempts to disengage the other latent space for each view to capture the view-specific information by introducing mutual information based regularization terms, so as to further improve the performance of multi-view subspace clustering. Extensive experiments on real-world multi-view data demonstrate that our method achieves superior performance over the related state-of-the-art methods.

HiTailor: Interactive Transformation and Visualization for Hierarchical Tabular Data.

Tabular visualization techniques integrate visual representations with tabular data to avoid additional cognitive load caused by splitting users' attention. However, most of the existing studies focus on simple flat tables instead of hierarchical tables, whose complex structure limits the expressiveness of visualization results and affects users' efficiency in visualization construction. We present HiTailor, a technique for presenting and exploring hierarchical tables. HiTailor constructs an abstract model, which defines row/column headings as biclustering and hierarchical structures. Based on our abstract model, we identify three pairs of operators, Swap/Transpose, ToStacked/ToLinear, Fold/Unfold, for transformations of hierarchical tables to support users' comprehensive explorations. After transformation, users can specify a cell or block of interest in hierarchical tables as a TableUnit for visualization, and HiTailor recommends other related TableUnits according to the abstract model using different mechanisms. We demonstrate the usability of the HiTailor system through a comparative study and a case study with domain experts, showing that HiTailor can present and explore hierarchical tables from different viewpoints. HiTailor is available at https://github.com/bitvis2021/HiTailor.

A fast and efficient path elimination algorithm for large-scale multiple common longest sequence problems.

BACKGROUND: In various fields, searching for the Longest Common Subsequences (LCS) of Multiple (i.e., three or more) sequences (MLCS) is a classic but difficult problem to solve. The primary bottleneck in this problem is that present state-of-the-art algorithms require the construction of a huge graph (called a direct acyclic graph, or DAG), which the computer usually has not enough space to handle. Because of their massive time and space consumption, present algorithms are inapplicable to issues with lengthy and large-scale sequences. RESULTS: A mini Directed Acyclic Graph (mini-DAG) model and a novel Path Elimination Algorithm are proposed to address large-scale MLCS issues efficiently. In mini-DAG, we employ the branch and bound approach to reduce paths during DAG construction, resulting in a very mini DAG (mini-DAG), which saves memory space and search time. CONCLUSION: Empirical experiments have been performed on a standard benchmark set of DNA sequences. The experimental results show that our model outperforms the leading algorithms, especially for large-scale MLCS problems.

Deep Unsupervised Active Learning on Learnable Graphs.

Recently, deep learning has been successfully applied to unsupervised active learning. However, the current method attempts to learn a nonlinear transformation via an auto-encoder while ignoring the sample relation, leaving huge room to design more effective representation learning mechanisms for unsupervised active learning. In this brief, we propose a novel deep unsupervised active learning model via learnable graphs, named ALLGs. ALLG benefits from learning optimal graph structures to acquire better sample representation and select representative samples. To make the learned graph structure more stable and effective, we take into account k -nearest neighbor graph as a priori and learn a relation propagation graph structure. We also incorporate shortcut connections among different layers, which can alleviate the well-known over-smoothing problem to some extent. To the best of our knowledge, this is the first attempt to leverage graph structure learning for unsupervised active learning. Extensive experiments performed on six datasets demonstrate the efficacy of our method.

Structure Guided Deep Neural Network for Unsupervised Active Learning.

Unsupervised active learning has become an active research topic in the machine learning and computer vision communities, whose goal is to choose a subset of representative samples to be labeled in an unsupervised setting. Most of existing approaches rely on shallow linear models by assuming that each sample can be well approximated by the span (i.e., the set of all linear combinations) of the selected samples, and then take these selected samples as the representative ones for manual labeling. However, the data do not necessarily conform to the linear models in many real-world scenarios, and how to model nonlinearity of data often becomes the key point of unsupervised active learning. Moreover, the existing works often aim to well reconstruct the whole dataset, while ignore the important cluster structure, especially for imbalanced data. In this paper, we present a novel deep unsupervised active learning framework. The proposed method can explicitly learn a nonlinear embedding to map each input into a latent space via a deep neural network, and introduce a selection block to select the representative samples in the learnt latent space through a self-supervised learning strategy. In the selection block, we aim to not only preserve the global structure of the data, but also capture the cluster structure of the data in order to well handle the data imbalance issue during sample selection. Meanwhile, we take advantage of the clustering result to provide self-supervised information to guide the above processes. Finally, we attempt to preserve the local structure of the data, such that the data embedding becomes more precise and the model performance can be further improved. Extensive experimental results on several publicly available datasets clearly demonstrate the effectiveness of our method, compared with the state-of-the-arts.

Silencing of LINC01963 enhances the chemosensitivity of prostate cancer cells to docetaxel by targeting the miR-216b-5p/TrkB axis.

Docetaxel (DTX) treatment effectively prolongs the overall survival of patients with prostate cancer. However, most patients eventually develop resistance to chemotherapy and experience tumor progression or even death. Long noncoding RNAs (lncRNAs) affect docetaxel chemosensitivity. However, the biological role and regulatory mechanisms of lncRNAs in docetaxel-resistant prostate cancer remain unclear. Differences in lncRNAs were evaluated by lncRNA sequencing and evaluated using quantitative real-time polymerase chain reaction, and TrkB expression was measured through western blot analysis. Proliferation was measured using the MTS, while apoptosis and cell cycle were measured using flow cytometry. In addition, migration and invasion were measured using transwell assays. Forty-eight female BALB/c nude mice were used for subcutaneous tumorigenicity and lung metastasis assays. We found that LINC01963 was overexpressed in the PC3-DR cells. LINC01963 silencing enhanced the chemosensitivity of PC3-DR to docetaxel and inhibited tumorigenicity and lung metastasis, while LINC01963 overexpression enhanced the chemoresistance of PC3 cells to docetaxel. It was found that LINC01963 bind to miR-216b-5p. The miR-216b-5p inhibitor reversed the suppressive effect of sh-LINC01963 on PC3-DR cell proliferation, migration, and invasion. Furthermore, miR-216b-5p can bind to the 3'-UTR of NTRK2 and inhibit TrkB protein levels. TrkB enhances docetaxel resistance in prostate cancer and reverses the effects of LINC01963 silencing and miR-216b-5p overexpression. In conclusion, silencing LINC01963 inhibited TrkB protein level to enhance the chemosensitivity of PC3-DR to docetaxel by means of competitively binding to miR-216b-5p. This study illustrates that LINC01963 is a novel therapeutic target for treating prostate cancer patients with DTX resistance.

StLiter: A Novel Algorithm to Iteratively Build the Compacted de Bruijn Graph From Many Complete Genomes.

Recently, the compacted de Bruijn graph (cDBG) of complete genome sequences was successfully used in read mapping due to its ability to deal with the repetitions in genomes. However, current approaches are not flexible enough to fit frequently building the graphs with different k-mer lengths. Instead of building the graph directly, how can we build the compacted de Bruijin graph of longer k-mer based on the one of short k-mer? In this article, we present StLiter, a novel algorithm to build the compacted de Bruijn graph either directly from genome sequences or indirectly based on the graph of a short k-mer. For 100 simulated human genomes, StLiter can construct the graph of k-mer length 15-18 in 2.5-3.2 hours with maximal ∼70GB memory in the case of without considering the reverese complements of the reference genomes. And it costs 4.5-5.9 hours when considering the reverse complements. In experiments, we compared StLiter with TwoPaCo, the state-of-art method for building the graph, on 4 datasets. For k-mer length 15-18, StLiter can build the graph 5-9 times faster than TwoPaCo using less maximal memory cost. For k-mer length larger than 18, given the graph of a short (k- x)-mer, such as x= 1-2, compared with TwoPaCo building the graph directly, StLiter can also build the graph more efficiently. The source codes of StLiter can be downloaded from web site https://github.com/BioLab-cz/StLiter.

Generalized Domain Conditioned Adaptation Network.

Domain adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supervision. Recent advances in DA mainly proceed by aligning the source and target distributions. Despite the significant success, the adaptation performance still degrades accordingly when the source and target domains encounter a large distribution discrepancy. We consider this limitation may attribute to the insufficient exploration of domain-specialized features because most studies merely concentrate on domain-general feature learning in task-specific layers and integrate totally-shared convolutional networks (convnets) to generate common features for both domains. In this paper, we relax the completely-shared convnets assumption adopted by previous DA methods and propose Domain Conditioned Adaptation Network (DCAN), which introduces domain conditioned channel attention module with a multi-path structure to separately excite channel activation for each domain. Such a partially-shared convnets module allows domain-specialized features in low-level to be explored appropriately. Further, given the knowledge transferability varying along with convolutional layers, we develop Generalized Domain Conditioned Adaptation Network (GDCAN) to automatically determine whether domain channel activations should be separately modeled in each attention module. Afterward, the critical domain-specialized knowledge could be adaptively extracted according to the domain statistic gaps. As far as we know, this is the first work to explore the domain-wise convolutional channel activations separately for deep DA networks. Additionally, to effectively match high-level feature distributions across domains, we consider deploying feature adaptation blocks after task-specific layers, which can explicitly mitigate the domain discrepancy. Extensive experiments on four cross-domain benchmarks, including DomainNet, Office-Home, Office-31, and ImageCLEF, demonstrate the proposed approaches outperform the existing methods by a large margin, especially on the large-scale challenging dataset. The code and models are available at https://github.com/BIT-DA/GDCAN.

Can high b-value 3.0 T biparametric MRI with the Simplified Prostate Image Reporting and Data System (S-PI-RADS) be used in biopsy-naïve men?

OBJECTIVE: To analyze the clinical value of high b-value 3.0 T biparametric magnetic resonance with the Simplified Prostate Image Reporting and Data System (S-PI-RADS) in biopsy-naïve men. METHODS: A retrospective analysis of the data of 224 patients who underwent prostate biopsy (cognitive fusion targeted biopsy combined with systematic biopsy) after a high b-value 3.0 T magnetic resonance examination at Haikou Hospital from July 2018 to July 2020 was performed. Two radiologists performed multi-parameter magnetic resonance imaging (mp-MRI) with the prostate imaging report and data system version 2 (PI-RADS v2) and biparametric magnetic resonance imaging (bp-MRI) with the simplified prostate image reporting and data system (S-PI-RADS). The detection efficacy of the two regimens was evaluated by classifying prostate cancer (PCa) and clinically significant prostate cancer (csPCa) according to pathology, and the statistical significance of the differences between the two regimens was determined by Z-test. RESULTS: The area under the receiver operating curve (AUC) values of mp-MRI based on PI-RADS v2 and bp-MRI based on S-PI-RADS to detect PCa were 0.905 and 0.892, respectively, while the AUC values for the detection of csPCa were 0.919 and 0.906, respectively. There was no statistically significant difference between the two tests (Z values were 0.909 and 1.145, p > 0.05). CONCLUSION: There was no significant difference in the detection efficacy of high b-value bp-MRI based on the S-PI-RADS score for prostate cancer and clinically significant prostate cancer compared with the standard PI-RADS v2 score with mp-MRI protocols, which can be applied clinically.

DiagAF: A More Accurate and Efficient Pre-Alignment Filter for Sequence Alignment.

Sequence alignment is an essential step in computational genomics. More accurate and efficient sequence pre-alignment methods that run before conducting expensive computation for final verification are still urgently needed. In this article, we propose a more accurate and efficient pre-alignment algorithm for sequence alignment, called DiagAF. Firstly, DiagAF uses a new lower bound of edit distance based on shift hamming masks. The new lower bound makes use of fewer shift hamming masks comparing with state-of-the-art algorithms such as SHD and MAGNET. Moreover, it takes account the information of edit distance path exchanging on shift hamming masks. Secondly, DiagAF can deal with alignments of sequence pairs with not equal length, rather than state-of-the-art methods just for equal length. Thirdly, DiagAF can align sequences with early termination for true alignments. In the experiment, we compared DiagAF with state-of-the-art methods. DiagAF can achieve a much smaller error rate than them, meanwhile use less time than them. We believe that DiagAF algorithm can further improve the performance of state-of-the-art sequence alignment softwares. The source codes of DiagAF can be downloaded from web site https://github.com/BioLab-cz/DiagAF.

I/O Efficient Early Bursting Cohesive Subgraph Discovery in Massive Temporal Networks.

Temporal networks are an effective way to encode temporal information into graph data losslessly. Finding the bursting cohesive subgraph (BCS), which accumulates its cohesiveness at the fastest rate, is an important problem in temporal networks. The BCS has a large number of applications, such as representing emergency events in social media, traffic congestion in road networks and epidemic outbreak in communities. Nevertheless, existing methods demand the BCS lasting for a time interval, which neglects the timeliness of the BCS. In this paper, we design an early bursting cohesive subgraph (EBCS) model based on the k-core to enable identifying the burstiness as soon as possible. To find the EBCS, we first construct a time weight graph (TWG) to measure the bursting level by integrating the topological and temporal information. Then, we propose a global search algorithm, called GS-EBCS, which can find the exact EBCS by iteratively removing nodes from the TWG. Further, we propose a local search algorithm, named LS-EBCS, to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time complexity. Subsequently, considering the situation that the massive temporal networks cannot be completely put into the memory, we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms, namely I/O-GS and I/O-LS respectively, to find the EBCS under the semi-external model. Extensive experiments, conducted on four real temporal networks, demonstrate the efficiency and effectiveness of our proposed algorithms. For example, on the DBLP dataset, I/O-LS and LS-EBCS have comparable running time, while the maximum memory usage of I/O-LS is only 6.5 MB, which is much smaller than that of LS-EBCS taking 308.7 MB. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-022-2367-3.

Deep Unsupervised Active Learning via Matrix Sketching.

Most existing unsupervised active learning methods aim at minimizing the data reconstruction loss by using the linear models to choose representative samples for manually labeling in an unsupervised setting. Thus these methods often fail in modelling data with complex non-linear structure. To address this issue, we propose a new deep unsupervised Active Learning method for classification tasks, inspired by the idea of Matrix Sketching, called ALMS. Specifically, ALMS leverages a deep auto-encoder to embed data into a latent space, and then describes all the embedded data with a small size sketch to summarize the major characteristics of the data. In contrast to previous approaches that reconstruct the whole data matrix for selecting the representative samples, ALMS aims to select a representative subset of samples to well approximate the sketch, which can preserve the major information of data meanwhile significantly reducing the number of network parameters. This makes our algorithm alleviate the issue of model overfitting and readily cope with large datasets. Actually, the sketch provides a type of self-supervised signal to guide the learning of the model. Moreover, we propose to construct an auxiliary self-supervised task by classifying real/fake samples, in order to further improve the representation ability of the encoder. We thoroughly evaluate the performance of ALMS on both single-label and multi-label classification tasks, and the results demonstrate its superior performance against the state-of-the-art methods. The code can be found at https://github.com/lrq99/ALMS.

A novel mHealth App (RyPros) for prostate cancer management: an accessibility and acceptability study.

BACKGROUND: Over the past decade, there has been a significant increase in research on the use of mobile health (mHealth) apps as disease management tools. However, very few apps are currently available for prostate cancer (PCa) patient management, and the available apps do not combine the needs of physicians with the requirements of patients. This study aimed to describe the development of a mHealth application for PCa survivors called RyPros, which includes dynamic visualization, intelligent reminders, and instant messaging to support decision-making regarding treatment and follow-up and test the initial accessibility and acceptability application. METHODS: The application was developed through a three-step procedure: logical structure design, application programming, and testing. Dynamic visualization, intelligent reminders, and instant messaging were the core functions of RyPros. Twenty-eight participants who had PCa were enrolled in four weeks of follow-up using the RyPros App. We initially evaluated participants' acceptance of RyPros based on their use of the app (login data, questionnaire completion) and a satisfaction survey. RESULTS: We successfully designed and tested the application. A total of 32 participants were enrolled, of whom 28 completed the 4-week follow-up, yielding a participation rate of 87.5%. Each participant logged on an average of 2.82 times and achieved an average of 0.89 questionnaires per week over the four weeks. Most participants (64%) liked the app, and most participants (71%) were satisfied, giving the RyPros app a rating of 4 or 5. More than half of the participants (61%) intended to use the RyPros app regularly, and the majority of participants agreed that the three core functionalities of RyPros were helpful (20/28, 71% for instant messaging; 16/28, 57% for visualization; and 18/28, 64% for reminders and assessments). CONCLUSIONS: The mHealth application we developed for PCa survivor management provided dynamic visualization, reminders, assessments, and instant messaging to support decision-making based on multidisciplinary collaboration. PCa survivors showed high acceptance of the RyPros app.

Long-term exposure to 4G smartphone radiofrequency electromagnetic radiation diminished male reproductive potential by directly disrupting Spock3-MMP2-BTB axis in the testes of adult rats.

The correlation between long-term exposure to SRF-EMR and the decline in male fertility is gradually receiving increasing attention from the medical society. While male reproductive organs are often exposed to SRF-EMR, little is currently known about the direct effects of long-term SRF-EMR exposure on the testes and its involvement in the suppression of male reproductive potential. The present study was designed to investigate this issue by using 4G SRF-EMR in rats. A unique exposure model using a 4G smartphone achieved localized exposure to the scrotum of the rats for 6 h each day (the smartphone was kept on active talk mode and received an external call for 1 min over 10 min intervals). Results showed that SRF-EMR exposure for 150 days decreased sperm quality and pup weight, accompanied by testicular injury. However, these adverse effects were not evident in rats exposed to SRF-EMR for 50 days or 100 days. Sequencing analysis and western blotting suggested Spock3 overexpression in the testes of rats exposed to SRF-EMR for 150 days. Inhibition of Spock3 overexpression improved sperm quality decline and alleviated testicular injury and BTB disorder in the exposed rats. Additionally, SRF-EMR exposure suppressed MMP2 activity, while increasing the activity of the MMP14-Spock3 complexes and decreasing MMP14-MMP2 complexes; these results were reversed by Spock3 inhibition. Thus, long-term exposure to 4G SRF-EMR diminished male fertility by directly disrupting the Spock3-MMP2-BTB axis in the testes of adult rats. To our knowledge, this is the first study to show direct toxicity of SRF-EMR on the testes emerging after long-term exposure.