Occurrence and Biodegradation of Nonylphenol in the Environment

Nonylphenol (NP) is an ultimate degradation product of nonylphenol polyethoxylates (NPE) that is primarily used in cleaning and industrial processes. Its widespread use has led to the wide existence of NP in various environmental matrices, such as water, sediment, air and soil. NP can be decreased by biodegradation through the action of microorganisms under aerobic or anaerobic conditions. Half-lives of biodegradation ranged from a few days to almost one hundred days. The degradation rate for NP was influenced by temperature, pH and additions of yeast extracts, surfactants, aluminum sulfate, acetate, pyruvate, lactate, manganese dioxide, ferric chloride, sodium chloride, hydrogen peroxide, heavy metals, and phthalic acid esters. Although NP is present at low concentrations in the environment, as an endocrine disruptor the risks of long-term exposure to low concentrations remain largely unknown. This paper reviews the occurrence of NP in the environment and its aerobic and anaerobic biodegradation in natural environments and sewage treatment plants, which is essential for assessing the potential risk associated with low level exposure to NP and other endocrine disruptors

RGB-DI Images and Full Convolution Neural Network-Based Outdoor Scene Understanding for Mobile Robots

This paper presents a multisensor-based approach to outdoor scene understanding of mobile robots. Since laser scanning points in 3-D space are distributed irregularly and unbalanced, a projection algorithm is proposed to generate RGB, depth, and intensity (RGB-DI) images so that the outdoor environments can be optimally measured with a variable resolution. The 3-D semantic segmentation in RGB-DI cloud points is, therefore, transformed to the semantic segmentation in RGB-DI images. A full convolution neural network (FCN) model with deep layers is designed to perform semantic segmentation of RGB-DI images. According to the exact correspondence between each 3-D point and each pixel in a RGB-DI image, the semantic segmentation results of the RGB-DI images are mapped back to the original point clouds to realize the 3-D scene understanding. The proposed algorithms are tested on different data sets, and the results show that our RGB-DI image and FCN model-based approach can provide a superior performance for outdoor scene understanding. Moreover, real-world experiments were conducted on our mobile robot platform to show the validity and practicability of the proposed approach

A novel seminar learning framework for weakly supervised salient object detection

Weakly supervised salient object detection (SOD) is a challenging task and has drawn much attention from several research perspectives, it has revealed two problems while driving the rapid development of saliency detection. (1) Large divergence in the characteristics of saliency regions in terms of location, shape and size makes them difficult to recognize. (2) The properties of convolutional neural networks dictate that it is insensitive to various transformations, which will lead to hardly balance the application of various disturbances. To tackle these limitations, this paper proposes a novel seminar learning framework with consistent transformation ensembling (SLF-CT) for scribble supervised SOD. The framework consists of the teacher–student model and the student–student model for segmenting the salient objects. Specifically, we first design a cross attention guided network (CAGNet) as a baseline model for saliency prediction. Then we assign CAGNet to the teacher–student model, where the teacher network is based on the exponential moving average and guides the training of the student network. Moreover, we adopt multiple pseudo labels to transfer the information among students from different conditions. To further enhance the regularization of the network, a consistency transformation mechanism is also incorporated, which encourages the saliency prediction and input image of the network to be consistent. The experimental results demonstrate that the proposed approach performs favorably comparable with the state-of-the-art weakly supervised methods. As far as we know, the proposed approach is the first application of seminar learning in the SOD area.</p

Transcriptomic profile of premature ovarian insufficiency with RNA-sequencing

IntroductionThis study aimed to explore the transcriptomic profile of premature ovarian insufficiency (POI) by investigating alterations in gene expression.MethodsA total of sixty-one women, comprising 31 individuals with POI in the POI group and 30 healthy women in the control group (HC group), aged between 24 and 40 years, were recruited for this study. The transcriptomic profiles of peripheral blood samples from all study subjects were analyzed using RNA-sequencing.ResultsThe results revealed 39 differentially expressed genes in individuals with POI compared to healthy controls, with 10 upregulated and 29 downregulated genes. Correlation analysis highlighted the relationship between the expression of SLC25A39, CNIH3, and PDZK1IP1 and hormone levels. Additionally, an effective classification model was developed using SLC25A39, CNIH3, PDZK1IP1, SHISA4, and LOC389834. Functional enrichment analysis demonstrated the involvement of these differentially expressed genes in the “haptoglobin-hemoglobin complex,” while KEGG pathway analysis indicated their participation in the “Proteoglycans in cancer” pathway.ConclusionThe identified genes could play a crucial role in characterizing the genetic foundation of POI, potentially serving as valuable biomarkers for enhancing disease classification accuracy

Elevation of Inducible Nitric Oxide Synthase and Cyclooxygenase-2 Expression in the Mouse Brain after Chronic Nonylphenol Exposure

The present study was performed to investigate the effects of chronic administration of nonylphenol (NP) on the expression of inflammation-related genes in the brains of mice. NP was given orally by gavages at 0, 50, 100, and 200 mg/kg/d. The expression of inflammatory enzymes, inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), was evaluated by immunohistochemistry and immunoblotting assays. The nitric oxide (NO) level and nitric oxide synthase (NOS) activity were also measured by biochemical analyses. The results showed that NP at a high dose (200 mg/kg/d) significantly increased the expression of iNOS and COX-2 in both the hippocampus and cortex. In parallel with the increase in iNOS expression, the NO level was significantly greater at the dose of 200 mg/kg/d, compared to the control. The activity of NOS was also increased in the brain of mice at the dose of 100 and 200 mg/kg/d. These findings demonstrate that NP may have the potential to induce the chronic inflammation or cause neurotoxicity in the mouse brain