Effects of Arbuscular Mycorrhizal Fungi on Accumulation of Heavy Metals in Rhizosphere Soil

The rhizosphere soil arbuscular mycorrhizal fungi will affect the absorption of heavy metal substances by the host plants. The effects of the arbuscular mycorrhizal fungi are inhibitory and conversion effects. The type and quantity of AMF fungi are different, and there are also differences in the absorption of arbuscular mycorrhizal fungi in the rhizosphere soil. Changes in the accumulation of heavy metals will affect the growth of arbuscular mycorrhizal fungi in the rhizosphere soil. In this paper, a preliminary investigation is made as to whether the AMF fungus number will affect the absorption of heavy metal Cd. Experiments show that with the increase of soil spores, the available cadmium content of soil also tends to increase

EFFECTS OF SHOE COLLAR HEIGHT ON SAGITTAL ANKLE ROM, KINETICS AND POWER OUTPUT DURING SINGLE-LEG AND DOUBLE-LEG JUMPS

The aim of this research was to examine the effects of high-top shoes and low-top shoes on sagittal ankle ROM, kinetics and power output during single-leg and double-leg jumps. Twelve male subjects were requested to wear high-top and low-top shoes to perform single-leg and double-leg jumps. Ankle joint kinematics and kinetics data were collected using Vicon system and force plates. Shoe collar heights did not influence the jump height in both single-leg and double-leg jump tasks. However, high-top shoes adopted in this study resulted in a significant smaller sagittal ankle ROM during a quasi-static movement. In addition, wearing high-top shoe could also decrease the dorsiflexion ankle joint torque and power output during the push-off phase in single-leg jump. These findings provide preliminary evidence suggesting that a changed ankle kinematic and kinetic behaviour in the sagittal plane may be induced when wearing high-top shoes

Effect of capecitabine combined with irinotecan on the safety of colon cancer treatment, patients’ adverse reactions and quality of life

Purpose: To study the impact of the combination of capecitabine and irinotecan on the safety of colon cancer treatment, adverse reactions and wellbeing of patients.Methods: Colon cancer subjects (n =120) admitted to Guangzhou First People’s Hospital, Guangzhou, China were assigned equally to two groups (A and B) according to their order of admission, and they received intravenous infusion of irinotecan. In addition, group A patients were administered capecitabine, but those in B group were given tegafur, gimeracil and oteracil porassium. The patients in groups A and B were compared with respect to the incidence of unwanted effects, quality of life (QoL), and overall clinical efficacy of the treatments.Results: Cases of nausea and vomiting, delayed diarrhea and sensory neuropathy of the patients were significantly reduced in group A, relative to group B. Moreover, QoL score after treatment was markedly higher in group A than in group B, while the objective response rate (ORR) of colon cancer patients in group A was also significantly higher than that in group B (p < 0.05). However, no obvious difference in disease control rate (DCR) was observed between groups A and B (p > 0.05).Conclusion: Combined capecitabine and irinotecan therapy effectively improves clinical prognosis, reduces the incidence of adverse reactions, and is safe in colon cancer patients. Therefore, the combined treatment may be beneficial in the management of colon cancer

A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks

Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy. It has gained substantial popularity in recent years, especially since the enforcement of data protection laws and regulations in many countries. To foster the application of FL, a variety of FL frameworks have been proposed, allowing non-experts to easily train ML models. As a result, understanding bugs in FL frameworks is critical for facilitating the development of better FL frameworks and potentially encouraging the development of bug detection, localization and repair tools. Thus, we conduct the first empirical study to comprehensively collect, taxonomize, and characterize bugs in FL frameworks. Specifically, we manually collect and classify 1,119 bugs from all the 676 closed issues and 514 merged pull requests in 17 popular and representative open-source FL frameworks on GitHub. We propose a classification of those bugs into 12 bug symptoms, 12 root causes, and 18 fix patterns. We also study their correlations and distributions on 23 functionalities. We identify nine major findings from our study, discuss their implications and future research directions based on our findings

FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation

Although industrial anomaly detection (AD) technology has made significant progress in recent years, generating realistic anomalies and learning priors of normal remain challenging tasks. In this study, we propose an end-to-end industrial anomaly detection method called FractalAD. Training samples are obtained by synthesizing fractal images and patches from normal samples. This fractal anomaly generation method is designed to sample the full morphology of anomalies. Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples. The differences between a teacher and a student model are converted into anomaly attention using a cosine similarity attention module. The proposed method enables an end-to-end semantic segmentation network to be used for anomaly detection without adding any trainable parameters to the backbone and segmentation head, and has obvious advantages over other methods in training and inference speed.. The results of ablation studies confirmed the effectiveness of fractal anomaly generation and backbone knowledge distillation. The results of performance experiments showed that FractalAD achieved competitive results on the MVTec AD dataset and MVTec 3D-AD dataset compared with other state-of-the-art anomaly detection methods.Comment: 12 pages, 5 figure

TransFA: Transformer-based Representation for Face Attribute Evaluation

Face attribute evaluation plays an important role in video surveillance and face analysis. Although methods based on convolution neural networks have made great progress, they inevitably only deal with one local neighborhood with convolutions at a time. Besides, existing methods mostly regard face attribute evaluation as the individual multi-label classification task, ignoring the inherent relationship between semantic attributes and face identity information. In this paper, we propose a novel \textbf{trans}former-based representation for \textbf{f}ace \textbf{a}ttribute evaluation method (\textbf{TransFA}), which could effectively enhance the attribute discriminative representation learning in the context of attention mechanism. The multiple branches transformer is employed to explore the inter-correlation between different attributes in similar semantic regions for attribute feature learning. Specially, the hierarchical identity-constraint attribute loss is designed to train the end-to-end architecture, which could further integrate face identity discriminative information to boost performance. Experimental results on multiple face attribute benchmarks demonstrate that the proposed TransFA achieves superior performances compared with state-of-the-art methods