Meta Pairwise Relationship Distillation for Unsupervised Person Re-Identification.

Unsupervised person re-identification (Re-ID) is challenging due to the lack of ground-truth labels. Most existing methods rely on pseudo labels estimated via iterative clustering and thus are highly susceptible to performance penalties incurred by the inaccurate estimated number of clusters. Alternatively, we utilize the sample pairs with pairwise pseudo labels to guide the feature learning to avoid the dilemma of determining cluster numbers. In this article, we propose a meta pairwise relationship distillation (MPRD) method that incorporates a graph convolutional network (GCN) to provide high-fidelity pairwise relationships to supervise the model training. A small amount of metadata with very-confidence pairwise relationships and the unlabeled pairs with the provided pseudo pairwise relationships participate in the GCN training. Besides, we introduce a hard sample deduction (HSD) module to timely mine the sample pairs with error-prone pairwise pseudo labels to mitigate the misled optimization by noisy labels. Furthermore, since the features of each positive pair represent the same person, we design a positive pair alignment (PPA) module to reduce the redundant information in each feature, which is achieved by minimizing the difference between each positive pair's feature distributions. Extensive experiments on the Market-1501, DukeMTMC-reID, and MSMT17 datasets show that our method outperforms the state-of-the-art unsupervised methods.

Disentangled Sample Guidance Learning for Unsupervised Person Re-identification.

Unsupervised person re-identification (Re-ID) is challenging due to the lack of ground truth labels. Most existing methods employ iterative clustering to generate pseudo labels for unlabeled training data to guide the learning process. However, how to select samples that are both associated with high-confidence pseudo labels and hard (discriminative) enough remains a critical problem. To address this issue, a disentangled sample guidance learning (DSGL) method is proposed for unsupervised Re-ID. The method consists of disentangled sample mining (DSM) and discriminative feature learning (DFL). DSM disentangles (unlabeled) person images into identity-relevant and identity-irrelevant factors, which are used to construct disentangled positive/negative groups that contain discriminative enough information. DFL incorporates the mined disentangled sample groups into model training by a surrogate disentangled learning loss and a disentangled second-order similarity regularization, to help the model better distinguish the characteristics of different persons. By using the DSGL training strategy, the mAP on Market-1501 and MSMT17 increases by 6.6% and 10.1% when applying the ResNet50 framework, and by 0.6% and 6.9% with the vision transformer (VIT) framework, respectively, validating the effectiveness of the DSGL method. Moreover, DSGL surpasses previous state-of-the-art methods by achieving higher Top-1 accuracy and mAP on the Market-1501, MSMT17, PersonX, and VeRi-776 datasets. The source code for this paper is available at https://github.com/jihaoxuanye/DiseSGL.

Scavenger Receptor C1 Mediates Toxicity of Binary Toxin from Lysinibacillus sphaericus to Ag55 Cells.

Lysinibacillus sphaericus harboring Binary (BinA and BinB) toxins is highly toxic against Anopheles and Culex mosquito larvae. The Anopheles Ag55 cell line is a suitable model for investigating the mode of Bin toxin action. Based on the low-levels of α-glycosidase Agm3 mRNA in Ag55 cells and the absence of detectable Agm3 proteins, we hypothesized that a scavenger receptor could be mediating Bin cytotoxicity. Preliminary RNA interference knockdown of the expressed scavenger receptors, combined with Bin cytotoxicity assays, was conducted. The scavenger Receptor C1 (SCRC1) became the focus of this study, as a putative receptor for Bin toxins in Ag55 cells, and SCRBQ2 was selected as a negative control. Open reading frames encoding SCRC1 and SCRBQ2 were cloned and expressed in vitro, and polyclonal antibodies were prepared for immunological analyses. The RNAi silencing of SCRC1 and SCRBQ2 resulted in the successful knockdown of both SCRC1 and SCRBQ2 transcripts and protein levels. The cytolytic toxicity of Bin against Ag55 cells was severely reduced after the SCRC1-RNAi treatment. The phagocytic receptor protein SCRC1 mediates endocytosis of the Bin toxin into Ag55 cells, thereby facilitating its internal cytological activity. The results support a mechanism of the Bin toxin entering Ag55 cells, possibly via SCRC1-mediated endocytosis, and encourage investigations into how Bin is transferred from its bound form on the midgut epithelial cells into the epithelial endocytic system.

Investigation on the molecular mechanism of SPA interference with osteogenic differentiation of bone marrow mesenchymal stem cells.

Binding of Staphylococcus aureus protein A (SPA) to osteoblasts induces apoptosis and inhibits bone formation. Bone marrow-derived mesenchymal stem cells (BMSCs) have the ability to differentiate into bone, fat and cartilage. Therefore, it was important to analyze the molecular mechanism of SPA on osteogenic differentiation. We introduced transcript sequence data to screen out differentially expressed genes (DEGs) related to SPA-interfered BMSC. Protein-protein interaction (PPI) network of DEGs was established to screen biomarkers associated with SPA-interfered BMSC. Receiver operating characteristic (ROC) curve was plotted to evaluate the ability of biomarkers to discriminate between two groups of samples. Finally, we performed GSEA and regulatory analysis based on biomarkers. We identified 321 DEGs. Subsequently, 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14) were identified by hubba algorithm in PPI. ROC analysis showed that six biomarkers could clearly discriminate between normal differentiated and SPA-interfered BMSC. Moreover, we found that these biomarkers were mainly enriched in the pyrimidine metabolism pathway. We also constructed '71 circRNAs-14 miRNAs-5 mRNAs' and '10 lncRNAs-5 miRNAs-2 mRNAs' networks. Kntc1 and Asf1b genes were associated with rno-miR-3571. Nek2 and Asf1b genes were associated with rno-miR-497-5p. Finally, we found significantly lower expression of six biomarkers in the SPA-interfered group compared to the normal group by RT-qPCR. Overall, we obtained 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap, and Kif14) related to SPA-interfered BMSC, which provided a theoretical basis to explore the key factors of SPA affecting osteogenic differentiation.

The clinical application value of 3.0T magnetic resonance T2 mapping imaging in evaluating the degree of acetabular cartilage degeneration in joint replacement surgery running title: MRI and acetabular cartilage degeneration.

BACKGROUND: To explore and compare the values of 3.0T magnetic resonance imaging (MRI) T2 mapping in evaluating the degree of acetabular cartilage degeneration in hip replacement surgery. METHODS: A total of 26 elderly patients with femoral neck fractures who were scanned in 3.0T MRI T2 mapping quantification technique were included. Basing on MRI images, the degree of acetabular cartilage degeneration was classified into Grade 0, 1, 2, 3 and 4, according to the International Cartilage Repair Society (ICRS) scores. In addition, 8 healthy volunteers were included for control group. RESULTS: By comparison with health population, T2 relaxation values in the anterior, superior, and posterior regions of acetabular cartilage in patients with femoral neck fracture were obviously increased (P < 0.001). Among the patients with femoral neck fractures, there were 16 hip joint with Grade 1-2 (mild degeneration subgroup) and 10 hip joints with Grade 3-4 (severe degeneration subgroup), accounting for 61.54% and 38.46%, respectively. Additionally, T2 relaxation values in the anterior and superior bands of articular cartilage were positively related to the MRI-based grading (P < 0.05); while there was no significant difference of T2 relaxation values in the posterior areas of articular cartilage between severe degeneration subgroup and mild degeneration subgroup (P > 0.05). Importantly, acetabular cartilage degeneration can be detected through signal changes of T2 mapping pseudo-color images. CONCLUSION: 3.0T MRI T2 mapping technology can be used to determine the degree of acetabular cartilage degeneration, which can effectively monitor the disease course.

Pulsatile gonadotropin-releasing hormone therapy induces spermatogenesis in pituitary stalk interruption syndrome: A case report and review of the literature.

BACKGROUND: Pituitary stalk interruption syndrome (PSIS) is a rare anatomical defect of the pituitary gland falling under the spectrum of holoprosencephaly phenotypes. It is characterized by a deficiency in anterior pituitary hormones, such as growth hormone, gonadotropins, and thyroid hormones. Due to the syndrome's rarity and nonspecific manifestations, there is a lack of standardized treatment strategies. Consequently, early diagnosis through imaging and on-time intervention are crucial for improving patients' outcomes. CASE SUMMARY: A 30-year-old man presented with absent secondary sexual characteristics and azoospermia. Laboratory evaluation revealed a deficiency in gonadotropins, while thyroid function was mostly within normal ranges. Magnetic resonance imaging of the pituitary gland showed pituitary stalk agenesis, hypoplasia of the anterior pituitary, and ectopic posterior pituitary, leading to the diagnosis of PSIS. Initially, the patient underwent 6 mo of gonadotropin therapy without significant changes in hormone levels and secondary sexual characteristics. Pulsatile gonadotropin-releasing hormone therapy was then administered, resulting in the detection of sperm in the semen analysis within 3 mo. After 6 mo, routine semen tests showed normal semen quality. The couple faced challenges in conceiving due to abstinence and underwent three cycles of artificial insemination, which was unsuccessful. They also attempted in vitro fertilization, but unfortunately, the woman experienced a miscarriage 10 wk after the embryo transfer. CONCLUSION: Early detection, accurate diagnosis, and timely treatment are crucial in improving the quality of life and fertility of PSIS patients.

Toward High Quality Multi-Object Tracking and Segmentation Without Mask Supervision.

Recently studies have shown the potential of weakly supervised multi-object tracking and segmentation, but the drawbacks of coarse pseudo mask label and limited utilization of temporal information remain to be unresolved. To address these issues, we present a framework that directly uses box label to supervise the segmentation network without resorting to pseudo mask label. In addition, we propose to fully exploit the temporal information from two perspectives. Firstly, we integrate optical flow-based pairwise consistency to ensure mask consistency across frames, thereby improving mask quality for segmentation. Secondly, we propose a temporally adjacent pair-based sampling strategy to adapt instance embedding learning for data association in tracking. We combine these techniques into an end-to-end deep model, named BoxMOTS, which requires only box annotation without mask supervision. Extensive experiments demonstrate that our model surpasses current state-of-the-art by a large margin, and produces promising results on KITTI MOTS and BDD100K MOTS. The source code is available at https://github.com/Spritea/BoxMOTS.

Hemodynamics in nutcracker syndrome: implications for diagnosis.

BACKGROUND: Nutcracker syndrome is a disease characterized by complex symptoms, making its diagnosis challenging and often delayed, often resulting in a painful experience for the patients. OBJECTIVE: This study aimed to investigate the pathogenesis of nutcracker syndrome through the perspective of hemodynamics by simulating blood flow with varying compression degrees of the left renal vein. METHODS: 3D patient-specific vascular models of the abdominal aorta, superior mesenteric artery and left renal vein were constructed based on CT images of patients suspected of having nutcracker syndrome. A hemodynamic simulation was then conducted using computational fluid dynamics to identify the correlation between alterations in hemodynamic parameters and varying degrees of compression. RESULTS: The study indicated the presence of an evident gradient in velocity distribution over the left renal vein with relatively high degrees of stenosis (α ≤ 50°), with maximum velocity in the central region of the stenosis. Additionally, when the compression degree of the left renal vein increases, the pressure distribution of the left renal vein presents an increasing number of gradient layers. Furthermore, the wall shear stress shows a correlation with the variation of blood flow velocity, i.e., the increase of wall shear stress correlates with the acceleration of the blood flow velocity. CONCLUSIONS: Using computational fluid dynamics as a non-invasive instrument to obtain the hemodynamic characteristics of nutcracker syndrome is feasible and could provide insights into the pathological mechanisms of the nutcracker syndrome supporting clinicians in diagnosis.

Transformer Based Pluralistic Image Completion With Reduced Information Loss.

Transformer based methods have achieved great success in image inpainting recently. However, we find that these solutions regard each pixel as a token, thus suffering from an information loss issue from two aspects: 1) They downsample the input image into much lower resolutions for efficiency consideration. 2) They quantize 2563 RGB values to a small number (such as 512) of quantized color values. The indices of quantized pixels are used as tokens for the inputs and prediction targets of the transformer. To mitigate these issues, we propose a new transformer based framework called "PUT". Specifically, to avoid input downsampling while maintaining computation efficiency, we design a patch-based auto-encoder P-VQVAE. The encoder converts the masked image into non-overlapped patch tokens and the decoder recovers the masked regions from the inpainted tokens while keeping the unmasked regions unchanged. To eliminate the information loss caused by input quantization, an Un-quantized Transformer is applied. It directly takes features from the P-VQVAE encoder as input without any quantization and only regards the quantized tokens as prediction targets. Furthermore, to make the inpainting process more controllable, we introduce semantic and structural conditions as extra guidance. Extensive experiments show that our method greatly outperforms existing transformer based methods on image fidelity and achieves much higher diversity and better fidelity than state-of-the-art pluralistic inpainting methods on complex large-scale datasets (e.g., ImageNet).

Towards Unified Robustness Against Both Backdoor and Adversarial Attacks.

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connection between them: (1) planting a backdoor into a model will significantly affect the model's adversarial examples; (2) for an infected model, its adversarial examples have similar features as the triggered images. Based on these observations, a novel Progressive Unified Defense (PUD) algorithm is proposed to defend against backdoor and adversarial attacks simultaneously. Specifically, our PUD has a progressive model purification scheme to jointly erase backdoors and enhance the model's adversarial robustness. At the early stage, the adversarial examples of infected models are utilized to erase backdoors. With the backdoor gradually erased, our model purification can naturally turn into a stage to boost the model's robustness against adversarial attacks. Besides, our PUD algorithm can effectively identify poisoned images, which allows the initial extra dataset not to be completely clean. Extensive experimental results show that, our discovered connection between backdoor and adversarial attacks is ubiquitous, no matter what type of backdoor attack. The proposed PUD outperforms the state-of-the-art backdoor defense, including the model repairing-based and data filtering-based methods. Besides, it also has the ability to compete with the most advanced adversarial defense methods. The code is available here.

PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition.

Notwithstanding the prominent performance shown in various applications, point cloud recognition models have often suffered from natural corruptions and adversarial perturbations. In this paper, we delve into boosting the general robustness of point cloud recognition, proposing Point-Cloud Contrastive Adversarial Training (PointCAT). The main intuition of PointCAT is encouraging the target recognition model to narrow the decision gap between clean point clouds and corrupted point clouds by devising feature-level constraints rather than logit-level constraints. Specifically, we leverage a supervised contrastive loss to facilitate the alignment and the uniformity of hypersphere representations, and design a pair of centralizing losses with dynamic prototype guidance to prevent features from deviating outside their belonging category clusters. To generate more challenging corrupted point clouds, we adversarially train a noise generator concurrently with the recognition model from the scratch. This differs from previous adversarial training methods that utilized gradient-based attacks as the inner loop. Comprehensive experiments show that the proposed PointCAT outperforms the baseline methods, significantly enhancing the robustness of diverse point cloud recognition models under various corruptions, including isotropic point noises, the LiDAR simulated noises, random point dropping, and adversarial perturbations. Our code is available at: https://github.com/shikiw/PointCAT.

"UHPLC-Q-TOF/MS-chemometrics-network pharmacology" integrated strategy to discover quality markers of raw and stir-fried Fructus Tribuli and process optimization of stir-fried Fructus Tribuli.

INTRODUCTION: Fructus Tribuli, the dried ripe fruit of Tribulus terrestris L., has various beneficial effects, including liver-calming and depression-relieving effects. Raw Fructus Tribuli (RFT) and stir-fried Fructus Tribuli (SFT) are included in the Chinese Pharmacopoeia 2020 edition (Ch. P 2020). However, owing to the lack of specific regulations on SFT-processing parameters in Ch. P 2020, it is difficult to ensure the quality of commercially available SFT. OBJECTIVE: The present study aimed to screen the quality markers (Q-markers) of RFT and SFT and optimize the processing technology of SFT based on the identified Q-markers. METHODS: First, the ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) technology as well as multiple statistical analysis along with network pharmacology was used to comprehensively identify the Q-markers of RFT and SFT. Then, based on single-factor experiments, the Box-Behnken design (BBD) response surface methodology (RSM) was used to optimize the processing technology of SFT and perform process validation. RESULTS: A total of 63 components were identified in RFT and SFT extracts. Terrestrosin D and Terrestrosin K were initially considered the Q-markers of RFT and SFT, respectively. The optimum processing technology conditions were 208°C, 14 min, and 60 r·min-1. Three batches of process validation were performed, and the mean composite score was 56.87, with a relative standard deviation (RSD) value of 1.13%. CONCLUSION: The content of steroidal saponin components in RFT was significantly different before and after stir-frying. Terrestrosin D and Terrestrosin K were validated as the Q-markers of RFT and SFT, respectively. The identification of Q-markers for RFT and SFT offered a clear index for optimizing the SFT-processing technology and provided a basis for the quality control of RFT and SFT decoction pieces.

Robust Model Watermarking for Image Processing Networks via Structure Consistency.

The intellectual property of deep networks can be easily "stolen" by surrogate model attack. There has been significant progress in protecting the model IP in classification tasks. However, little attention has been devoted to the protection of image processing models. By utilizing consistent invisible spatial watermarks, the work (Zhang et al. 2020) first considered model watermarking for deep image processing networks and demonstrated its efficacy in many downstream tasks. Its success depends on the hypothesis that if a consistent watermark exists in all prediction outputs, that watermark will be learned into the attacker's surrogate model. However, when the attacker uses common data augmentation attacks (e.g., rotate, crop, and resize) during surrogate model training, it will fail because the underlying watermark consistency is destroyed. To mitigate this issue, we propose a new watermarking methodology, "structure consistency", based on which a new deep structure-aligned model watermarking algorithm is designed. Specifically, the embedded watermarks are designed to be aligned with physically consistent image structures, such as edges or semantic regions. Experiments demonstrate that our method is more robust than the baseline in resisting data augmentation attacks. Besides that, we test the generalization ability and robustness of our method to a broader range of adaptive attacks.

Adversarial Attack and Defense in Deep Ranking.

Deep Neural Network classifiers are vulnerable to adversarial attacks, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations. Specifically, the expected ranking order is first represented as a set of inequalities. Then a triplet-like objective function is designed to obtain the optimal perturbation. Conversely, an anti-collapse triplet defense is proposed to improve the ranking model robustness against all proposed attacks, where the model learns to prevent the adversarial attack from pulling the positive and negative samples close to each other. To comprehensively measure the empirical adversarial robustness of a ranking model with our defense, we propose an empirical robustness score, which involves a set of representative attacks against ranking models. Our adversarial ranking attacks and defenses are evaluated on MNIST, Fashion-MNIST, CUB200-2011, CARS196, and Stanford Online Products datasets. Experimental results demonstrate that our attacks can effectively compromise a typical deep ranking system. Nevertheless, our defense can significantly improve the ranking system's robustness and simultaneously mitigate a wide range of attacks.

Deep Image Matting With Sparse User Interactions.

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous. Recently, some hint-free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, some hints for removing semantic ambiguity and improving matting quality are essential. Apparently, there is a trade-off between interaction cost and matting quality. To balance performance and user-friendliness, we propose an improved deep image matting framework which is trimap-free and only needs sparse user click or scribble interaction to minimize the needed auxiliary constraints while still allowing interactivity. Moreover, we introduce uncertainty estimation that predicts which parts need polishing and conduct uncertainty-guided refinement. To trade off runtime against refinement quality, users can also choose different refinement modes. Experimental results show that our method performs better than existing trimap-free methods and comparably to state-of-the-art trimap-based methods with minimal user effort. Finally, we demonstrate the extensibility of our framework to video human matting without any structure modification, by adding optical flow-based sparse hint propagation and temporal consistency regularization imposed on the single frame.

Recurrent stroke shortly after mechanical thrombectomy secondary to carotid web: A case report.

RATIONALE: Carotid web, a known source of thrombus for embolic stroke, presents a considerable risk of stroke recurrence. While case reports have demonstrated the safety and effectiveness of mechanical thrombectomy in treating carotid web-related stroke, the need for concurrent carotid artery stenting to prevent recurrent stroke immediately after thrombectomy remains unclear. This study aims to underscore the importance of immediate carotid artery stenting in preventing recurrent stroke following mechanical thrombectomy in patients with carotid web-related stroke. PATIENT CONCERNS: A 43-year-old woman with acute onset of left limb weakness and slurred speech within 3 hours was admitted to the emergency department. DIAGNOSES: Computed tomographic angiography confirmed the M1 segment occlusion of the right middle cerebral artery. INTERVENTIONS: The patient received intravenous thrombolysis in the local hospital and mechanical thrombectomy in our stroke center. OUTCOMES: Three days post-mechanical thrombectomy, there was a sudden exacerbation of her neurological deficit symptoms. A reexamination via computed tomographic angiography revealed a re-occlusion in M1 segment of the right middle cerebral artery, despite the implementation of stringent anticoagulation therapy for carotid web-related stroke. LESSONS: Stroke patients with carotid web had a high risk of stroke recurrence and it was necessary to conduct carotid artery stenting to prevent stroke recurrence secondary to the carotid web immediately after mechanical thrombectomy.

A Novel Compute-and-Forward Relaying Method for Multi-Antenna Wireless Relay Networks.

Compute-and-Forward (CoF) is an innovative physical layer network coding strategy, designed to enable receivers in wireless communications to effectively utilize interference. The key idea of CoF is to implement integer combinations based on the codewords from multiple transmitters, rather than decoding individual source codewords. Although CoF is widely used in wireless relay networks, there are still some problems to be solved, such as rank failure, single antenna reception, and the shortest vector problem. In this paper, we introduce a successive extended CoF (SECoF) as a pioneering solution tailored for multi-source, multi-relay, and multi-antenna wireless relay networks. First, we analyze the traditional CoF, and design a SECoF method combining the concepts of matrix projection and successive interference cancellation, which overcomes the problem of CoF rate tending to zero and rank failure and improves the network performance. Secondly, we obtain an approximate solution to the integer-value coefficient vectors by using the LLL lattice-based resolution algorithm. In addition, we deduce the corresponding concise formulas of SECoF. Simulation results show that the SECoF has strong robustness and the approaches outperform the state-of-the-art methods in terms of computation rate, rank failure probability, and outage probability.

Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map.

Counting objects in crowded scenes remains a challenge to computer vision. The current deep learning based approach often formulate it as a Gaussian density regression problem. Such a brute-force regression, though effective, may not consider the annotation displacement properly which arises from the human annotation process and may lead to different distributions. We conjecture that it would be beneficial to consider the annotation displacement in the dense object counting task. To obtain strong robustness against annotation displacement, generalized Gaussian distribution (GGD) function with a tunable bandwidth and shape parameter is exploited to form the learning target point annotation probability map, PAPM. Specifically, we first present a hand-designed PAPM method (HD-PAPM), in which we design a function based on GGD to tolerate the annotation displacement. For end-to-end training, the hand-designed PAPM may not be optimal for the particular network and dataset. An adaptively learned PAPM method (AL-PAPM) is proposed. To improve the robustness to annotation displacement, we design an effective transport cost function based on GGD. The proposed PAPM is capable of integration with other methods. We also combine PAPM with P2PNet through modifying the matching cost matrix, forming P2P-PAPM. This could also improve the robustness to annotation displacement of P2PNet. Extensive experiments show the superiority of our proposed methods.

Dynamic Release Characteristics and Kinetics of a Persulfate Sustained-Release Material.

Sustained-release materials are increasingly being used in the delivery of oxidants for in situ chemical oxidation (ISCO) for groundwater remediation. Successful implementation of sustained-release materials depends on a clear understanding of the mechanism and kinetics of sustained release. In this research, a columnar sustained-release material (PS@PW) was prepared with paraffin wax and sodium persulfate (PS), and column experiments were performed to investigate the impacts of the PS@PW diameter and PS/PW mass ratio on PS release. The results demonstrated that a reduction in diameter led to an increase in both the rate and proportion of PS release, as well as a diminished lifespan of release. The release process followed the second-order kinetics, and the release rate constant was positively correlated with the PS@PW diameter. A matrix boundary diffusion model was utilized to determine the PS@PW diffusion coefficient of the PS release process, and the release lifespan of a material with a length of 500 mm and a diameter of 80 mm was predicted to be more than 280 days. In general, this research provided a better understanding of the release characteristics and kinetics of persulfate from a sustained-release system and could lead to the development of columnar PS@PW as a practical oxidant for in situ chemical oxidation of contaminated aquifers.

A Golgi-Targeted Platinum Complex Plays a Dual Role in Autophagy Regulation for Highly Efficient Cancer Therapy.

Regulating autophagy to control the homeostatic recycling process of cancer cells is a promising anticancer strategy. Golgi apparatus is a substrate of autophagy but the Golgi-autophagy (Golgiphagy) mediated antitumor pathway is rarely reported. Herein, we have developed a novel Golgi-targeted platinum (II) complex Pt3, which is ca. 20 times more cytotoxic to lung carcinoma than cisplatin and can completely eliminate tumors after intratumoral administration in vivo. Its nano-encapsulated system for tail vein administration also features a good anti-tumor effect. Mechanism studies indicate that Pt3 induces substantial Golgi stress, indicated by the fragmentation of Golgi structure, down-regulation of Golgi proteins (GM130, GRASP65/55), loss of Golgi-dependent transport and glycosylation. This triggers Golgiphagy but blocks the subsequent fusion of autophagosomes with lysosomes, that is a dual role in autophagy regulation, resulting in loss of proteostasis and apoptotic cell death. As far as we know, Pt3 is the first Golgi-targeted Pt complex that can trigger Golgi stress-mediated dual-regulation of autophagic flux and autophagy-apoptosis crosstalk for highly efficient cancer therapy.