"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.

[Long non-coding RNA in spermatogenesis regulation and male infertility: Progress in research].

Long non-coding RNA (lncRNA) is an RNA molecule transcribed by RNA polymerase II, longer than 200 nt, and not translated into proteins. During gonadal development and spermatogenesis, lncRNAs are involved in epigenetic mechanisms, including DNA methylation, chromatin remodeling, and histone tail modification, which play important regulatory roles at the transcriptional or post-transcriptional level. Epigenomics including lncRNA is considered to be the second dimension of DNA sequence that can be adapted to environmental factors to specifically regulate gene expressions in some cells. Based on the functional action mechanism of lncRNAs, we reviewed the advances in the studies of lncRNAs in the direction of spermatogenesis and male infertility and analyzed the potential of lncRNAs as a biomarker of male infertility. The potential application of lncRNA in the treatment of male infertility diseases can be further explored based on the lncRNA target, RNA interference, competitive binding closed target and structural disruption of lncRNAs.

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.

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.

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.

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). Codes are available at https://github.com/liuqk3/PUT.

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 [1] 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.

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.

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.

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.

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.

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.

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.