Thermal storage/management system with phase change materials for building

Peer reviewe

Case report: Gene mutation analysis and skin imaging of isolated café-au-lait macules

Background: Café-au-lait macules (CALMs) are common birthmarks associated with several genetic syndromes, such as neurofibromatosis type 1 (NF1). Isolated CALMs are defined as multiple café-au-lait macules in patients without any other sign of NF1. Typical CALMs can have predictive significance for NF1, and non-invasive techniques can provide more accurate results for judging whether café-au-lait spots are typical.Objectives: The study aimed to investigate gene mutations in six Chinese Han pedigrees of isolated CALMs and summarize the characteristics of CALMs under dermoscopy and reflectance confocal microscopy (RCM).Methods: In this study, we used Sanger sequencing to test for genetic mutations in six families and whole exome sequencing (WES) in two families. We used dermoscopy and RCM to describe the imaging characteristics of CALMs.Results: In this study, we tested six families for genetic mutations, and two mutations were identified as novel mutations. The first family identified [NC_000017.11(NM_001042492.2):c.7355G>A]. The second family identified [NC_000017.11(NM_001042492.2):c.2739_2740del]. According to genotype-phenotype correlation analyses, proband with frameshift mutation tended to have a larger number of CALMs and a higher rate of having atypical CALMs. Dermoscopy showed uniform and consistent tan-pigmented network patches with poorly defined margins with a lighter color around the hair follicles. Under RCM, the appearance of NF1 comprised the increased pigment granules in the basal layer and significantly increased refraction.Conclusion: A new heterozygous mutation and a new frameshift mutation of NF1 were reported. This article can assist in summarizing the properties of dermoscopy and RCM with CALMs

Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding

Histopathological image retrieval is a key technology for computer-aided diagnosis. However, patients are reluctant to reveal their privacy in histopathological image retrieval. In order to further improve the effectiveness and safety of histopathological image retrieval, this paper proposes a new histopathological retrieval scheme based on asymmetric residual hash (ARH) and DNA coding techniques. In this paper, we first present a novel ARH for histopathological image retrieval to improve the effectiveness of histopathological search scheme, and then we use the 5-D hyperchaotic system to protect patient privacy. Specifically, the contribution consists of four aspects: 1) A histopathology ciphertext domain search scheme was proposed to improve the performance of computer-aided diagnosis. 2) An asymmetric approach was implemented to process histopathological query points and database points, and a novel asymmetric residual hash algorithm was first proposed to improve the accuracy and speed of histopathological image retrieval. 3) The 5-D hyperchaotic system and DNA coding technique are applied to histopathological image retrieval to protect patient privacy. 4) The loss function is constructed and optimized to learn network parameters and hash codes. The simulation experiment was performed on three datasets (Kimia Path24, Kimia Path960, and Malaria), and the results proved the effectiveness of the ARH algorithm. In addition, our proposed search method can resist common types of attacks during histopathological data transmission. Specifically, the MAP of the ARH is 0.9678 on the KIMIA Path24 with the value of hyperparameter is 125 and the length of hash code is 32. The MAP of the ARH is 0.966 on the KIMIA Path960 with the value of hyperparameter is 225 and the length of hash code is 32. The MAP of the ARH is 0.9482 on the Malaria with the value of hyperparameter is 10 and the length of hash code is 24

Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval

Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks

Targeted Therapy and Immunotherapy for Heterogeneous Breast Cancer

Breast cancer (BC) is the most common malignancy in women worldwide, and it is a molecularly diverse disease. Heterogeneity can be observed in a wide range of cell types with varying morphologies and behaviors. Molecular classifications are broadly used in clinical diagnosis, including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), vascular endothelial growth factor receptor (VEGFR), and breast cancer gene (BRCA) mutations, as indicators of tumor heterogeneity. Treatment strategies differ according to the molecular subtype. Besides the traditional treatments, such as hormone (endocrine) therapy, radiotherapy, and chemotherapy, innovative approaches have accelerated BC treatments, which contain targeted therapies and immunotherapy. Among them, monoclonal antibodies, small-molecule inhibitors and antibody–drug conjugates, and targeted delivery systems are promising armamentarium for breast cancer, while checkpoint inhibitors, CAR T cell therapy, cancer vaccines, and tumor-microenvironment-targeted therapy provide a more comprehensive understanding of breast cancer and could assist in developing new therapeutic strategies

CANet: A Combined Attention Network for Remote Sensing Image Change Detection

Change detection (CD) is one of the essential tasks in remote sensing image processing and analysis. Remote sensing CD is a process of determining and evaluating changes in various surface objects over time. The impressive achievements of deep learning in image processing and computer vision provide an innovative concept for the task of CD. However, existing methods based on deep learning still have problems detecting small changed regions correctly and distinguishing the boundaries of the changed regions. To solve the above shortcomings and improve the efficiency of CD networks, inspired by the fact that an attention mechanism can refine features effectively, we propose an attention-based network for remote sensing CD, which has two important components: an asymmetric convolution block (ACB) and a combined attention mechanism. First, the proposed method extracts the features of bi-temporal images, which contain two parallel encoders with shared weights and structures. Then, the feature maps are fed into the combined attention module to reconstruct the change maps and obtain refined feature maps. The proposed CANet is evaluated on the two publicly available datasets for challenging remote sensing image CD. Extensive empirical results with four popular metrics show that the designed framework yields a robust CD detector with good generalization performance. In the CDD and LEVIR-CD datasets, the F1 values of the CANet are 3.3% and 1.3% higher than those of advanced CD methods, respectively. A quantitative analysis and qualitative comparison indicate that our method outperforms competitive baselines in terms of both effectiveness and robustness

The Effect of Dietary Protein Intake on the Risk of Gestational Diabetes

Background. The results of epidemiological studies on the association between dietary protein intake and gestational diabetes mellitus (GDM) are controversial. Thus, this systematic review and meta-analysis of cohort studies were established to attain comprehensive findings regarding the association between dietary protein and the risk of GDM. Methods. Bibliographic databases including PubMed, Scopus, Web of Science, and Google Scholar were searched to discover papers related to dietary protein and the risk of GDM. The summary relative risks with 95% confidence intervals (CIs) were estimated through a random effect model for the analysis of the highest versus the lowest categories of dietary proteins. Results. A significantly increased risk of GDM among women who consumed the highest amount of animal protein was observed (summarized risk estimate: 1.52; 95% CI: 1.07, 2.17; I2 = 50.8%). No significant associations were identified regarding vegetable protein (summarized risk estimate:0.99, 95% CI: 0.80 to 1.23, I2 = 63.8%) and total protein (summarized risk estimate: 1.12; 95% CI: 0.88, 1.41; I2 = 35.4%). Conclusion. This review revealed that total protein intake had no relationship with the risk of GDM, while animal protein increases this risk. Further larger prospective cohort studies are required to confirm our results

The Effect of Periodontitis on Dementia and Cognitive Impairment: A Meta-Analysis

The association between periodontal disease and dementia/cognitive impairment continues to receive increasing attention. However, whether periodontal disease is a risk factor for dementia/cognitive impairment is still uncertain. This meta-analysis was conducted to comprehensively analyze the effect of periodontitis on dementia and cognitive impairment, and to assess the periodontal status of dementia patients at the same time. A literature search was undertaken on 19 October 2020 using PubMed, Web of Science, and Embase with different search terms. Two evaluators screened studies according to inclusion and exclusion criteria, and a third evaluator was involved if there were disagreements; this process was the same as that used for data extraction. Included studies were assessed with the Newcastle-Ottawa Scale (NOS), and results were analyzed using software Review Manager 5.2. Twenty observational studies were included. In the comparison between periodontitis and cognitive impairment, the odds ratio (OR) was 1.77 (95% confidence interval (CI), 1.31–2.38), which indicated that there was a strong relationship between periodontitis and cognitive impairment. There was no statistical significance in the effect of periodontitis on dementia (OR = 1.59; 95%CI, 0.92–2.76). The subgroup analysis revealed that moderate or severe periodontitis was significantly associated with dementia (OR = 2.13; 95%CI, 1.25–3.64). The mean difference (MD) of the community periodontal index (CPI) and clinical attachment level (CAL) was 0.25 (95%CI, 0.09–0.40) and 1.22 (95%CI, 0.61–1.83), respectively. In this meta-analysis, there was an association between periodontitis and cognitive impairment, and moderate or severe periodontitis was a risk factor for dementia. Additionally, the deterioration of periodontal status was observed among dementia patients

The modulator role of Urtica dioica on deleterious effects of retinoic acid high doses on histological parameters and fertilization of rats

Aim: This study purposed to evaluate the modulator and protective role of Urtica dioica (UD) extract against deleterious effects of retinoic acid (RA) high doses on histological parameters and fertilization of rats. Materials and methods: For the in-vivo phase, 60 female Wistar rats were divided into 6 identical groups as 1) control, 2) 25 mg/kg RA, 3) 25 mg/kg UD extract, 4) 50 mg/kg UD extract, 5) UD extract (25 mg/kg) + RA (25 mg/kg), and 6) UD extract (50 mg/kg) + RA (25 mg/kg). Biochemical parameters, including luteinizing hormone (LH), folliclestimulating hormone (FSH), malondialdehyde (MDA) levels, superoxide dismutase (SOD), and catalase (CAT) activities, were measured. In the in-vitro phase, oocytes were obtained from 10 female rats without injection. In addition to the mentioned parameters, histological parameters (oocytes in various stages) and the results of IVM, IVF, and embryo developments were assessed and compared among the groups with the use of one-way ANOVA and Tukey's post hoc tests. Results: The high dosage of RA significantly reduced the LH and FSH levels; however, UD alone and with RA increased the hormone levels in rats. Regarding the reactive oxygen species (ROS) activity levels in rats' blood samples, RA increased the MDA and decreased the SOD and CAT levels. Treatment with UD extract (UD + RA groups) significantly improved the parameters mentioned, showing UD's antioxidant effect. The rate of oocyte maturation, 2-cell–4-cell and 4-cell–8-cell embryos, and blastocyst formation increased significantly in the groups in which UD extracts were administered compared to the control and RA groups. Furthermore, the increases were significant in the UD + RA groups compared to the RA group. Conclusion: UD extract can significantly reduce RA high doses side effects on histological parameters and fertilization of rats and has the protective potential against RA deleterious effects

Research on Multipoint Leak Location of Gas Pipeline Based on Variational Mode Decomposition and Relative Entropy

Pipeline leak detection has always been a relatively difficult technical problem; especially in urban pipeline leak detection, there are still many problems to be solved. A multipoint leak detection and location method for urban gas pipelines based on variational mode decomposition and relative entropy was proposed. Firstly, the experiment pipeline system was built, and the original signal was collected by acoustic emission technology; then, a variational model method was used to decompose the signal to obtain multiple intrinsic mode function (IMF) components with different characteristic scales. According to the characteristics of relative entropy, each component was analyzed, the appropriate IMF component was selected, and the selected component was reconstructed to obtain the observation signal. The multipoint leakage location model of the urban gas pipeline was established. The number of source signals was estimated by singular value decomposition, and the leakage signals were separated; finally, the accurate location of leakage point was achieved by cross-correlation positioning. The results showed that the average relative error of the pipeline leak location results is 2.97%, and the leak location accuracy is significantly improved, achieving the purpose of precise location