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.

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.

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.

"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.

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.

Relationship between lumbar disc herniation and Roussouly classification in the sagittal alignment of the spine and pelvis in young people.

Background: The sagittal plane of spine-pelvis morphology of adult patients with lumbar disc herniation (LDH) differs from that of normal people. There is a correlation between abnormal sagittal plane morphology and adult LDH. This study aimed to investigate the relationship between LDH in young people and the Roussouly classification in the sagittal alignment of the spine and pelvis. Methods: A total of 126 patients with LDH hospitalized in our tertiary hospital between January 2020 and January 2021 who met the inclusion and exclusion criteria were retrospectively analyzed in a cross-sectional study. Participants were grouped according to the spinal level of disc herniation (L4/5 group, L5/S1 group, L3/4 group). Pelvic incidence (PI), pelvic tilt (PT), and sacral slope (SS) of all cases were measured using standing lateral spinal radiographs. Facet joint angles (FJA) were measured using lumbar computed tomography (CT) horizontal radiographs, and the height of the anterior, middle, and posterior margins of the herniated disc was measured using sagittal radiographs. The classification of Pfirrmann disc degeneration was determined via magnetic resonance imaging (MRI) film of the lumbar region. Roussouly classification was performed according to sagittal plane morphology, and data were analyzed among each group. Results: In all, 126 patients aged from 18 to 40 years, with an average age of 31.04±5.886 years were included. There were 63 cases of L4/5 disc herniation, 59 cases of L5/S1 herniation, and 4 cases of L3/4 herniation. There were 35 cases of epiphyseal separation. The Roussouly classification included 21 cases of type 1, 56 cases of type 2, 32 cases of type 3, 4 cases of type 4, and 13 cases of type 3 + anteverted pelvis (type 3-AP). The L4/5 disc herniation group had significantly more types 2 and 3 than the other groups, and the L5/S1 disc herniation group had significantly more types 1 and 2 than the other groups. There were no significant differences in the Pfirrmann grade of intervertebral disc degeneration; epiphyseal separation; the height of anterior, middle, and posterior margins of the herniated disc; the difference between left and right facet angle and bilateral facet angle; and the thoracic kyphosis (TK) value among any of the groups (P>0.05). There were significant differences in PI, PT, SS, lumbar lordosis (LL), and height of the anterior and posterior margins of the intervertebral space among all groups (P<0.05). PI values were grouped at 50°; PT, SS, and LL were significantly different (P<0.05), whereas TK values were not significantly different between the 2 groups (P>0.05). SS values were grouped at 35°, and there were significant differences in PI, TK, and LL between the 2 groups (P<0.05). According to the disc herniation segment, the patients were divided into L4/5 and L5/S1 groups. There were significant differences in PI, PT, anterior edge height, anterior to posterior edge height, left facet angle, right facet angle, and bilateral facet angle (P<0.05). Conclusions: LDH is common in L4/5 and L5/S1 segments. The incidence of Roussouly type 1 and 2 LDH is significantly higher than other types, and type 4 is the lowest. Small PI and small SS are more obvious in the L5/S1 segment. L5/S1 disc herniation is more common in patients with Roussouly type 1 and 2 classifications, whereas L4/5 disc herniation is more frequent in patients with Roussouly type 2 and 3 classifications.

Network pharmacology-based approach uncovers the pharmacodynamic components and mechanism of Fructus Tribuli for improving endothelial dysfunction in hypertension.

ETHNOPHARMACOLOGICAL RELEVANCE: Fructus Tribuli (FT), a traditional Chinese medicinal herbal, has been used for the clinical treatment of cardiovascular diseases for many years and affects vascular endothelial dysfunction (ED) in patients with hypertension. AIM OF THE STUDY: This study aimed to demonstrate the pharmacodynamic basis and mechanisms of FT for the treatment of ED. MATERIALS AND METHODS: The present study used ultra-high-performance liquid chromatography coupled with quadruple-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) to analyze and identify the chemical components of FT. The active components in blood were determined after the oral administration of FT by comparative analysis to blank plasma. Then, based on the active components in vivo, network pharmacology was performed to predict the potential targets of FT in treating ED. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were also performed, and component-target-pathway networks were constructed. Interactions between the major active components and main targets were verified by molecular docking. Moreover, spontaneously hypertensive rats (SHRs) were divided into the normal, model, valsartan, low-dose FT, medium-dose FT, and high-dose FT experimental groups. In pharmacodynamic verification studies, treatment effects on blood pressure, serum markers (nitric oxide [NO], endothelin-1 [ET-1,], and angiotensin Ⅱ [Ang Ⅱ)]) of ED, and endothelial morphology of the thoracic aorta were evaluated and compared between groups. Finally, the PI3K/AKT/eNOS pathway was analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot of the thoracic aorta of rats in each group to detect the mRNA expression of PI3K, AKT, and eNOS and the protein expression of PI3K, AKT, p-AKT, eNOS, and p-eNOS. RESULTS: A total of 51 chemical components were identified in FT, and 49 active components were identified in rat plasma. Thirteen major active components, 22 main targets, and the PI3K/AKT signaling pathway were screened by network pharmacology. The animal experiment results showed that FT reduced systolic blood pressure and ET-1 and Ang Ⅱ levels and increased NO levels in SHRs to varying degrees. The therapeutic effects were positively correlated with the oral dose of FT. Hematoxylin-eosin (HE) staining confirmed that FT could alleviate the pathological damage of the vascular endothelium. qRT-PCR and Western blot analysis confirmed that up-regulated expression of the PI3K/AKT/eNOS signaling pathway could improve ED. CONCLUSIONS: In this study, the material basis of FT was comprehensively identified, and the protective effect on ED was confirmed. FT had a treatment effect on ED through multi-component, multi-target, and multi-pathways. It also played a role by up-regulating the PI3K/AKT/eNOS signaling pathway.

ContextLoc++: A Unified Context Model for Temporal Action Localization.

Effectively tackling the problem of temporal action localization (TAL) necessitates a visual representation that jointly pursues two confounding goals, i.e., fine-grained discrimination for temporal localization and sufficient visual invariance for action classification. We address this challenge by enriching the local, global and multi-scale contexts in the popular two-stage temporal localization framework. Our proposed model, dubbed ContextLoc++, can be divided into three sub-networks: L-Net, G-Net, and M-Net. L-Net enriches the local context via fine-grained modeling of snippet-level features, which is formulated as a query-and-retrieval process. Furthermore, the spatial and temporal snippet-level features, functioning as keys and values, are fused by temporal gating. G-Net enriches the global context via higher-level modeling of the video-level representation. In addition, we introduce a novel context adaptation module to adapt the global context to different proposals. M-Net further fuses the local and global contexts with multi-scale proposal features. Specially, proposal-level features from multi-scale video snippets can focus on different action characteristics. Short-term snippets with fewer frames pay attention to the action details while long-term snippets with more frames focus on the action variations. Experiments on the THUMOS14 and ActivityNet v1.3 datasets validate the efficacy of our method against existing state-of-the-art TAL algorithms.

Representing Multimodal Behaviors With Mean Location for Pedestrian Trajectory Prediction.

Representing multimodal behaviors is a critical challenge for pedestrian trajectory prediction. Previous methods commonly represent this multimodality with multiple latent variables repeatedly sampled from a latent space, encountering difficulties in interpretable trajectory prediction. Moreover, the latent space is usually built by encoding global interaction into future trajectory, which inevitably introduces superfluous interactions and thus leads to performance reduction. To tackle these issues, we propose a novel Interpretable Multimodality Predictor (IMP) for pedestrian trajectory prediction, whose core is to represent a specific mode by its mean location. We model the distribution of mean location as a Gaussian Mixture Model (GMM) conditioned on sparse spatio-temporal features, and sample multiple mean locations from the decoupled components of GMM to encourage multimodality. Our IMP brings four-fold benefits: 1) Interpretable prediction to provide semantics about the motion behavior of a specific mode; 2) Friendly visualization to present multimodal behaviors; 3) Well theoretical feasibility to estimate the distribution of mean locations supported by the central-limit theorem; 4) Effective sparse spatio-temporal features to reduce superfluous interactions and model temporal continuity of interaction. Extensive experiments validate that our IMP not only outperforms state-of-the-art methods but also can achieve a controllable prediction by customizing the corresponding mean location.

Effectiveness of letrozole in pituitary downregulated normogonadotrophic young women with an initial poor response.

It has been reported that 10 to 15% of young normogonadotrophic women show suboptimal response to standard long protocols. Letrozole (LE), an aromatase inhibitor, was shown to improve ovarian sensitivity to follicle stimulating hormone (FSH) and follicular response to gonadotrophin treatment in poor ovarian response patients. We reasoned that it might be possible to utilize LE in young normogonadotrophic patients with unexpected hypo-response in standard gonadotropin-releasing hormone agonist long protocol. A total of 652 patients defined as normogonadotrophic patients with unexpected hypo-response were divided into 2 groups, the +LE group and the +Gn group. +LE group: A fixed daily dose of 2.5 mg of LE was added on day 8 of stimulation. +Gn group: A fixed daily dose of 75 U of human menopausal gonadotrophin was added on day 8 of stimulation. The primary outcome measures were the number of oocytes obtained, fertilization rate, days of stimulation, and total FSH dosage. The secondary outcome measures were the implantation rate and ongoing pregnancy rate. There were no significant differences in the clinical and hormonal characteristics between the 2 groups. A shorter duration of stimulation and a lower dosage of recombinant FSH consumption on the day of human chorionic gonadotropin administration were all observed in the +LE group. Patients who received LE therapy showed a higher number of oocytes obtained and significantly higher fertilization rates. The implantation rate and ongoing pregnancy rate were comparable in both groups. LE significantly improves the number of oocytes obtained in patients with suboptimal response to standard gonadotropin-releasing hormone agonist long protocol.

Time-resolved simulation of blood flow through left anterior descending coronary artery: effect of varying extent of stenosis on hemodynamics.

BACKGROUND AND OBJECTIVES: Real-time blood flow variation is crucial for understanding the dynamic development of coronary atherosclerosis. The main objective of this study is to investigate the effect of varying extent of stenosis on the hemodynamic features in left anterior descending coronary artery. METHODS: Various Computational fluid dynamics (CFD) models were constructed with patient-specific CT image data, using actual fractional flow reserve (FFR) as boundary conditions to provide a real-time quantitative description of hemodynamic properties. The hemodynamic parameters, such as the local and instantaneous wall shear stress (WSS), oscillating shear index (OSI) and relative residence time (RRT), blood flow velocity and pressure drop during various phases of cardiac cycle were provided in detail. RESULTS: There was no evident variation in hemodynamic parameters in the cases of less than 50% stenosis while there were abrupt and dramatic changes in hemodynamics when the stenosis aggravated from 60 to 70%. Furthermore, when the stenosis was beyond 70%, there existed substantial pressure difference, WSS, and blood flow velocity in the center of the stenosis. Although OSI and RRT increased along with the aggravation of stenosis, they appeared with obvious abnormalities across all cases, even in mild stenosis. CONCLUSION: The simulation could present a dynamic and comprehensive profile of how hemodynamic parameters vary in accordance with divergent severities of stenosis, which could serve as an effective reference for the clinicians to have a deeper insight into the pathological mechanism of coronary atherosclerosis and stenosis.

[In-situ Remediation Effect of Cadmium-polluted Agriculture Land Using Different Amendments Under Rice-wheat Rotation].

Exploring the effects of one-time amendment treatments on cadmium (Cd)-contaminated farmland soils is beneficial for providing a theoretical basis to effectively prevent Cd pollution in farmland soils and ensure the safe production of crops. Five amendments, including straw biochar, fly ash, sepiolite, white marble powder, and shale (particle size <0.2 mm, application rate 2.25 kg·m-2), were applied to the Cd-contaminated farmland soils. The soil nutrients, pH, soil available Cd, and Cd chemical forms in the soils and grain Cd concentration in the planted crops were determined to investigate the effects and persistence of one-time applications of the five amendments. The results showed that:① the application of the five amendments had little effect on soil nutrient content, but all of them could increase soil pH. Amendment treatments improved the transfer of Cd from the acid extraction fraction to residue fraction and further reduced the Cd availability in the soil. The decreasing amplitudes of straw biochar and white marble powder soil conditioner were 20.42%-22.53%, which was higher than those in the other treatments. ② The grain Cd concentrations in rice and wheat were significantly decreased under the amendment treatments with the decreasing amplitudes of 19.88%-48.77% and 5.06%-24.00%, respectively. The Cd concentrations in rice grains under the treatments of straw biochar, fly ash, and white marble powder soil conditioner were 0.195, 0.196, and 0.223 mg·kg-1, respectively, which were lower than those under the other treatments and were close to or approached the National Standard of Food Safety(GB 2762-2017)(0.2 mg·kg-1). ③ The immobilization effects on Cd in farmland soils were decreasing with time under one-time application of the amendments. The available Cd concentrations in the soil and Cd concentrations in crop grains were still lower than those in the control after three rounds of rice-wheat rotation. The straw biochar and white marble powder soil conditioner had a good and long-term effect on reducing Cd availability in soils and Cd concentrations in crop grain, making them ideal materials for safe production in Cd-contaminated soils.