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Due to the accelerated development of population growth and urbanization, water resources are facing increasing pressure. In this context, the sustainable use of water resources has become a top priority. At the same time, the construction of urban green space and public space has also received increasing attention, as they not only enhance the image of the city but also provide services such as leisure and entertainment for people. In this context, how to combine the sustainable use of water resources with the visual design of a water-based landscape environment has become a new research direction. This paper analyzes the visual design of the water-based landscape environment. In terms of whether the water-based environmental visual design can create a different artistic conception for urban green space, 78% of white-collar workers in enterprises, 88% of sanitation workers, and 79% of technicians from different professions thought so. Results showed that in the construction of urban green space, people should pay attention to the interaction between people and the environment to achieve the coordination between people and society, nature, and culture. HIGHLIGHTS Based on the sustainable utilization of water resources, this paper analyzes and studies the visual design of the water-based landscape environment.; The experimental results of this paper show whether environmental visual design is widely used in urban green space.;

Background: Colon adenocarcinoma (COAD) is a prevalent malignancy worldwide, yet, its underlying pathogenesis and genetic characteristics are still unclear. Previous studies have suggested that NADH dehydrogenase 1 alpha subcomplex subunit 4-like 2 (NDUFA4L2) may affect tumor progression across various cancers. However, this effect on COAD has rarely been reported. Thus, this study investigated NDUFA4L2's prognostic and diagnostic relevance and explored its potential connection with immune cell infiltration in COAD. Methods: To achieve this, RNA sequencing data from Cancer Genome Atlas (TCGA) was analyzed to assess NDUFA4L2's prognostic value in COAD, and factors relevant to the prognosis of COAD, including NDUFA4L2, were scrutinized using Kaplan-Meier analyses as well as univariate and multivariate Cox regression. A nomogram model was created to project prognosis based on the results of multivariate Cox analysis. Furthermore, gene set enrichment analysis (GSEA) was employed to pinpoint key NDUFA4L2-related pathways, and single-sample GSEA (ssGSEA) on TCGA data was employed to investigate the connections of NDUFA4L2 with cancer immune infiltrations. Results: Our findings revealed significant associations of high NDUFA4L2 expression with poor overall survival, progression-free interval, and disease-specific survival of COAD patients. GSEA indicated close links of NDUFA4L2 with several signaling pathways implicated in tumorigenesis, including extracellular matrix receptor interaction, the intestinal immune network for immunoglobulin A production, natural killer (NK) cell-mediated cytotoxicity, pathways in cancer, cell adhesion molecules, mitogen-activated protein kinase signaling pathway, Hedgehog signaling pathway, transforming growth factor beta signaling pathway, and chemokine signaling pathway. Additionally, ssGSEA identified a positive link between increased NDUFA4L2 expression and higher infiltration degree of various immune cells, such as immature dendritic cells, macrophages, NK cells and dendritic cells. Conclusions: Collectively, our findings demonstrate the association of increased NDUFA4L2 expression with adverse prognosis and heightened immune cell infiltration in COAD patients.

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread.

Background: Prenatal diagnosis and genetic counseling play an important role in preventing and controlling birth defects. No reports were found of prenatal diagnosis of couples carrying both the thalassemia and deafness genes. In this study, we presented the prenatal screening and diagnosis of a couple with both thalassemia and deafness genes, contributing to better genetic counseling.Case Report: A couple visited our hospital for a routine prenatal examination. As required by the policy in our region, they underwent screening and genetic diagnosis for thalassemia. Meanwhile, they did not accept the recommendation to test for spinal muscular atrophy and deafness genes. The female was confirmed to be a Hb Quong Sze (Hb QS) carrier (αQSα/αα, βN/βN), and the male had Hb H disease combined with β-thalassemia (--SEA/αCSα, βCDs41-42 (-TTCT)/βN). A prenatal diagnosis of the fetus revealed a Hb CS heterozygote. Subsequent complementary testing showed that the male was a double heterozygote of the GJB2 gene c.299_300delAT combined with c.109G>A, and Sanger sequencing confirmed that the female was a carrier of c.508_511dup in the GJB2. Fortunately, the chorionic villi results indicated that the fetus was only a carrier of deafness.Conclusion: Since both partners carried thalassemia and deafness genes, the couple required prenatal diagnosis for the respective mutations. Expanded carrier screening (ECS) is a more advanced technology that can detect multiple disease genes simultaneously.

ABSTRACTObjective The 4.2 kb deletion (-α4.2/) is a common a+-thalassemia with a carrier rate, followed by the South-East Asian deletion (–SEA) and the 3.7 kb deletion (-α3.7/). There are few reports about 4.2 kb deletion sub-types. Herein, we present a patient with double heterozygous -α4.2Ⅰ/-α4.2Ⅱwho was identified using third-generation sequencing (TGS).Methods Hematology and hemoglobin fraction analysis were carried out by complete blood count (CBC) and capillary electrophoresis (CE). Gap-PCR was used to detect the common deletional α-thalassemia, and multiple ligation-dependent probe amplification (MLPA) was performed to screen the large deletion. Sanger sequencing identified the variant. The different deletions were confirmed by TGS.Results CBC showed the patient with microcytic hypochromic anemia, and CE indicated the presence of a Hb variant. Gap-PCR and MLPA detected 4.2 kb deletion homozygotes (-α4.2/-α4.2). The Hb variant was confirmed as Hb Q-Thailand by Sanger sequencing. The patient was identified as compound heterozygous of 4.2 kb deletion and Hb Q-Thailand (-α4.2/-α4.2-Q-Thailand, -α4.2Ⅰ/-α4.2Ⅱ) using TGS.Conclusions Hb Q-Thailand (-α4.2-Q-Thailand/) complex 4.2 kb deletion heterozygote (-α4.2/) is easily misdiagnosed as 4.2 kb homozygous using Gap-PCR and MLPA. The TGS enables the identification of the two different 4.2 kb deletion sub-types.

ABSTRACTObjectives To report the hematological and molecular characteristics of a novel α-globin variant found among Chinese families.Methods This study was done on two unrelated families (F1 and F2). Hematological results were obtained by an automated blood cell analyzer. Hemoglobin (Hb) fraction analysis was carried out using capillary electrophoresis (CE) and high-performance liquid chromatography (HPLC). Gap-PCR and reverse dot blot (RDB) methods were used to detect the common α-thalassemia mutations in the Chinese population. The Hb variants were defined by Sanger sequencing.Results Hb fraction analysis of F2 cord blood using HPLC showed an abnormal peak (3.5%) of the S-window, but CE presented a 12.2% abnormal peak at zone 5(S). Similar results of CE were observed from the F1 twin's cord blood. Compared with newborns, Hb analysis of F2 father using HPLC demonstrated an abnormal peak (16.9%) of S-window and an unknown peak (0.5%) at a retention time of 4.60 min, respectively. In contrast, CE revealed a high Hb F peak at zone 7 and an unknown peak at zone 1. There was no abnormality detected with Gap-PCR and RDB in these patients. However, Sanger sequencing confirmed the presence of a new heterozygous mutation (GAC>GGC) at codon 74 of the HBA2 gene (HBA2:c.224A>G), resulting in a novel Hb variant. We named it Hb Liangqing for the birthplace of the proband.Conclusion This is the first report of Hb Liangqing detected by HPLC and CE. The normal hematological phenotype suggests that it may be a benign Hb variant.

Abstract The presence of hemoglobin (Hb) variants might interfere with some glycated hemoglobin (HbA1c) measurements. There have been a few reports of compound Hb variants affecting HbA1c testing. Here, we report a case of the coinheritance of two Hb variants in the β‐globin gene. High‐performance liquid chromatography with the Hb program showed a high HbA2 level. Similarly, an E‐window peak was separated on the high‐performance liquid chromatography with a glycated Hb program. However, capillary electrophoresis showed two abnormal peaks and no HbA peak. Sanger sequencing confirmed the presence of Hb New York and HbE. This is the first report of a compound heterozygote for HbE and Hb New York. The double heterozygote caused erroneous results for HbA1c on high‐performance liquid chromatography and enzyme assay.

Metabolism-related fatty liver disease (MAFLD) is the most common form of chronic liver disease in the world. Its pathogenesis is influenced by both environmental and genetic factors. With the upgrading of gene screening methods and the development of human genome project, whole genome scanning has been widely used to screen genes related to MAFLD, and more and more genetic variation factors related to MAFLD susceptibility have been discovered. There are genetic variants that are highly correlated with the occurrence and development of MAFLD, and there are genetic variants that are protective of MAFLD. These genetic variants affect the development of MAFLD by influencing lipid metabolism and insulin resistance. Therefore, in-depth analysis of different mechanisms of genetic variation and targeting of specific genetic variation genes may provide a new idea for the early prediction and diagnosis of diseases and individualized precision therapy, which may be a promising strategy for the treatment of MAFLD.

Bile acids (BAs) are synthesized in liver tissue from cholesterol and are an important endocrine regulator and signaling molecule in the liver and intestine. It maintains BAs homeostasis, and the integrity of intestinal barrier function, and regulates enterohepatic circulation in vivo by modulating farnesoid X receptors (FXR) and membrane receptors. Cirrhosis and its associated complications can lead to changes in the composition of intestinal micro-ecosystem, resulting in dysbiosis of the intestinal microbiota. These changes may be related to the altered composition of BAs. The BAs transported to the intestinal cavity through the enterohepatic circulation are hydrolyzed and oxidized by intestinal microorganisms, resulting in changes in their physicochemical properties, which can also lead to dysbiosis of intestinal microbiota and overgrowth of pathogenic bacteria, induction of inflammation, and damage to the intestinal barrier, thus aggravating the progression of cirrhosis. In this paper, we review the discussion of BAs synthesis pathway and signal transduction, the bidirectional regulation of bile acids and intestinal microbiota, and further explore the role of reduced total bile acid concentration and dysregulated intestinal microbiota ratio in the development of cirrhosis, in order to provide a new theoretical basis for the clinical treatment of cirrhosis and its complications.

Objectives: This study aimed to establish a droplet digital polymerase chain reaction (ddPCR) assay for South-East Asian (SEA) deletion based on a fully integrated digital PCR system DropXpert S6.Methods: A total of 151 whole blood samples, 10 chorionic villus samples, and 17 amniotic fluid samples were collected, including 106 SEA heterozygotes, 43 normal individuals, 10 Hb Bart's hydrops details, and 19 SEA deletions combined with other genotypes.Genotypes of these samples were determined by the Gap-PCR method. We perform a series of optimizations of the ddPCR system to ensure the performance of the entire ddPCR reaction, such as droplet stability, fluorescence clustering, sensitivity, and accuracy.Results: Our assay exhibited 99.4% (177/178) accuracy compared with the Gap-PCR method, and the minimum detection limit of DNA was 0.1 ng/μL.Both targets have reliable linearity, R2 = 0.9999 for the α-thalassemia SEA deletion allele and R2 = 1 for the wild-type allele. The coefficient of variation for α-thalassemia SEA deletion allele detection at 2 and 10 ng/μL concentrations was 5.42% and 1.91%, respectively. In contrast, the coefficient of variation for wild-type allele detection was 4.06% and 1.83%, demonstrating its high quantitative accuracy. In addition, the DropXpert S6 PCR system showed some advantages over other ddPCR instruments, such as reducing testing costs, simplifying and automating the workflow.Conclusions: The DropXpert S6 PCR system provided a highly accurate diagnosis for α-thalassemia SEA deletion and can be used to detect α-thalassemia as an alternative method.

Gastric cancer (GC), a common and high-mortality disease, still occupies an important position in current cancer research, and Helicobacter pylori (H. pylori) infection as its important risk factor has been a hot and challenging research area. Among the numerous pathogenic factors of H. pylori, the virulence protein CagA has been widely studied as the only bacterial-derived oncoprotein. It was found that CagA entering into gastric epithelial cells (GECs) can induce the dysregulation of multiple cellular pathways such as MAPK signaling pathway, PI3K/Akt signaling pathway, NF-κB signaling pathway, Wnt/β-catenin signaling pathway, JAK-STAT signaling pathway, Hippo signaling pathway through phosphorylation and non-phosphorylation. These disordered pathways cause pathological changes in morphology, adhesion, polarity, proliferation, movement, and other processes of GECs, which eventually promotes the occurrence of GC. With the deepening of H. pylori-related research, the research on CagA-induced abnormal signaling pathway has been updated and deepened to some extent, so the key signaling pathways activated by CagA are used as the main stem to sort out the pathogenesis of CagA in this paper, aiming to provide new strategies for the H. pylori infection and treatment of GC in the future.

Exosomes are extracellular vesicles with a diameter of 30–150 nm that function in mediating intercellular communication and intercellular material exchange. The liposomal membrane of exosomes protects the cargo carried by exosomes from degradation and assists in transporting cargo to recipient cells to regulate a variety of physiological and pathological processes. The incidence of gynecologic cancers is increasing annually, which is extremely harmful to the lives and health of women because such cancers are challenging to detect at the early stage. Recently, exosomes have emerged as novel biomarkers for diagnosing and predicting the development of gynecologic cancers. In particular, non-coding RNAs (microRNAs [miRNAs], long non-coding RNAs [lncRNAs], and circular RNAs [circRNAs]) carried by exosomes have been extensively investigated in gynecologic cancers. Therefore, the purpose of this review is to focus on the potential roles of exosomes of different origins in ovarian cancer, cervical cancer, and endometrial cancer, which will help to determine the molecular mechanism of carcinogenesis.

Precision agriculture represents the new age of conventional agriculture. This is made possible by the advancement of various modern technologies such as the internet of things. The unparalleled potential for data collection and analytics has resulted in an increase in multi-disciplinary research within machine learning and agriculture. However, the application of machine learning techniques to agriculture seems to be out of step with core machine learning research. This gap is further exacerbated by the inherent challenges associated with agricultural data. In this work, we conduct a systematic review of a large body of academic literature published between 2000 and 2022, on the application of machine learning techniques to agriculture. We identify and discuss some of the key data issues such as class imbalance, data sparsity and high dimensionality. Further, we study the impact of these data issues on various machine learning approaches within the context of agriculture. Finally, we identify some of the common pitfalls in the machine learning and agriculture research including the misapplication of machine learning evaluation techniques. To this end, this survey presents a holistic view on the state of affairs in the cross-domain of machine learning and agriculture and proposes some suitable mitigation strategies to address these challenges.

Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain. The advanced technologies of machine learning and computer vision have been applied in precision livestock management, including critical disease detection, vaccination, production management, tracking, and health monitoring. This paper offers a systematic literature review (SLR) of vision-based cattle identification. More specifically, this SLR is to identify and analyse the research related to cattle identification using Machine Learning (ML) and Deep Learning (DL). This study retrieved 731 studies from four online scholarly databases. Fifty-five articles were subsequently selected and investigated in depth. For the two main applications of cattle detection and cattle identification, all the ML based papers only solve cattle identification problems. However, both detection and identification problems were studied in the DL based papers. Based on our survey report, the most used ML models for cattle identification were support vector machine (SVM), k-nearest neighbour (KNN), and artificial neural network (ANN). Convolutional neural network (CNN), residual network (ResNet), Inception, You Only Look Once (YOLO), and Faster R-CNN were popular DL models in the selected papers. Among these papers, the most distinguishing features were the muzzle prints and coat patterns of cattle. Local binary pattern (LBP), speeded up robust features (SURF), scale-invariant feature transform (SIFT), and Inception or CNN were identified as the most used feature extraction methods. This paper details important factors to consider when choosing a technique or method. We also identified major challenges in cattle identification. There are few publicly available datasets, and the quality of those datasets are affected by the wild environment and movement while collecting data. The processing time is a critical factor for a real-time cattle identification system. Finally, a recommendation is given that more publicly available benchmark datasets will improve research progress in the future.

The incidence of liver cancer is extremely high worldwide and poses a serious threat to human life and health. But at present, apart from radiotherapy, chemotherapy, liver transplantation, and early resection, sorafenib was the main systemic therapy proven to have clinical efficacy for unresectable liver cancer (HCC) until 2017. Despite the emerging immunotherapy in the past decade with immune inhibitors such as PD - 1 being approved and applied to clinical treatment, there are still some patients with no response. This review aims to elucidate the mechanisms underlying the tumor microenvironment of hepatocellular carcinoma and thus analyze the effectiveness of targeting the tumor microenvironment to improve the therapeutic efficacy of hepatocellular carcinoma, including the effectiveness and feasibility of immunotherapy, tumor oncolytic viruses and anti-vascular proliferation therapy.

Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of machine learning (ML) algorithms for detecting mouse-ear hawkweed (Pilosella officinarum) foliage and flowers from Unmanned Aerial Vehicle (UAV)-acquired multispectral (MS) images at various spatial resolutions. The performances of different ML algorithms, namely eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbours (KNN), were analysed in their capacity to detect hawkweed foliage and flowers using MS imagery. The imagery was obtained at numerous spatial resolutions from a highly infested study site located in the McKenzie Region of the South Island of New Zealand in January 2021. The spatial resolution of 0.65 cm/pixel (acquired at a flying height of 15 m above ground level) produced the highest overall testing and validation accuracy of 100% using the RF, KNN, and XGB models for detecting hawkweed flowers. In hawkweed foliage detection at the same resolution, the RF and XGB models achieved highest testing accuracy of 97%, while other models (KNN and SVM) achieved an overall model testing accuracy of 96% and 72%, respectively. The XGB model achieved the highest overall validation accuracy of 98%, while the other models (RF, KNN, and SVM) produced validation accuracies of 97%, 97%, and 80%, respectively. This proposed methodology may facilitate non-invasive detection efforts of mouse-ear hawkweed flowers and foliage in other naturalised areas, enabling land managers to optimise the use of UAV remote sensing technologies for better resource allocation.

Using nanoparticles (NPs) to effectively mitigate the negative effects of abiotic stressors on plant growth and development will help to achieve sustainable agriculture. Although there have been some prior reviews in this area, bibliometrics is still lacking. In this study, the most productive nations or regions, journals, publishers, and scholars in this field were identified using an objective bibliometric examination of the pertinent material published in the Web of Science core database. To dig deeper for information, the keywords co-occurrence, collaboration network of countries/regions and authors, and network map of highly cited papers citation are used to characterize present advances and forecast potential future trends. The results show a growing interest in using nanoparticles to alleviate abiotic stressors. There has been an exponential increase in the number of relevant papers and citations. Moreover, Asian countries are the most active in this subject, even if the USA generates papers with the best overall quality. The most common publishers and journals in this area are Elsevier and Environmental Science-Nano, while Wu HH is currently the most active author. Even though many researchers have formed close partnerships, there is not yet a large collaborative group of researchers in this field. Gaps in the current scientific literature are highlighted, such as the lack of use of omics, especially multi-omics, to provide a more in-depth and comprehensive explanation of the molecular mechanisms by which NPs enhance plant resistance to abiotic stresses. This bibliometric study will provide a valuable reference for studying the evolution of the field and identifying research frontiers.

Tea has become one of the most popular drinks worldwide because of its special taste and health benefits. ‘Yuhua’ tea is a famous specialty of Nanjing, China, a green tea produced from Camelia sinensis. It belongs to the green tea category and is one of the top ten most famous teas in China. The physical and chemical properties of soil are very important to the quality of tea. However, at present, there are few instances of research on the soil of the ‘Yuhua’ tea cultivation area. To provide the theoretical basis for improving the final quality of ‘Yuhua’ tea by soil management measures, the effects of the soil’s physical and chemical properties on the contents of important chemical components (free amino acids, tea polyphenols, and caffeine) of ‘Yuhua’ tea in three different cultivation areas in Nanjing were studied. The quality of ‘Yuhua’ tea is greatly influenced by the physical and chemical properties of the soil in which the tea plant grows. The free amino acid content and tea polyphenol content of tea leaves were significantly and negatively correlated with soil bulk density, pH, and water-soluble calcium content, while a significantly positive correlation was noticed between the two criteria and soil effective nitrogen, phosphorus, and potassium content. Additionally, the growth of the tea tree will be hampered and perhaps stressed if the soil’s helpful mineral concentration is too low. The tea tree may produce more caffeine as a result, which could lower the quality of the tea leaves. Therefore, implementing targeted soil management techniques is the key to promoting tea tree growth and improving tea quality. This study laid the foundation for the adoption of corresponding soil management measures to improve the quality of ‘Yuhua’ tea.

The accurate prediction of building heat demand plays the critical role in refined management of heating, which is the basis for on-demand heating operation. This paper proposed a prediction model framework for building heat demand based on reinforcement learning. The environment, reward function and agent of the model were established, and experiments were carried out to verify the effectiveness and advancement of the model. Through the building heat demand prediction, the model proposed in this study can dynamically control the indoor temperature within the acceptable interval (19–23 °C). Moreover, the experimental results showed that after the model reached the primary, intermediate and advanced targets in training, the proportion of time that the indoor temperature can be controlled within the target interval (20.5–21.5 °C) was over 35%, 55% and 70%, respectively. In addition to maintaining indoor temperature, the model proposed in this study also achieved on-demand heating operation. The model achieving the advanced target, which had the best indoor temperature control performance, only had a supply–demand error of 4.56%.

Background: Bromodomain and extra-terminal inhibitors (BETi) have shown efficacy for the treatment of aggressive triple negative breast cancer (TNBC). However, BETi are plagued by a narrow therapeutic window as manifested by severe toxicities at effective doses. Therefore, it is a limitation to their clinical implementation in patient care. Methods: The impact of vitamin C on the efficacy of small compounds including BETi was assessed by high-throughput screening. Co-treatment of TNBC by BETi especially JQ1 and vitamin C was evaluated in vitro and in vivo. Findings: High-throughput screening revealed that vitamin C improves the efficacy of a number of structurally-unrelated BETi including JQ1, I-BET762, I-BET151, and CPI-203 in treating TNBC cells. The synergy between BETi and vitamin C is due to suppressed histone acetylation (H3ac and H4ac), which is in turn caused by upregulated histone deacetylase 1 (HDAC1) expression upon vitamin C addition. Treatment with JQ1 at lower doses together with vitamin C induces apoptosis and inhibits the clonogenic ability of cultured TNBC cells. Oral vitamin C supplementation renders a sub-therapeutic dose of JQ1 able to inhibit human TNBC xenograft growth and metastasis in mice. Interpretation: Vitamin C expands the therapeutic window of BETi by sensitizing TNBC to BETi. Using vitamin C as a co-treatment, lower doses of BETi could be used to achieve an increased therapeutic index in patients, which will translate to a reduced side effect profile. Fund: University of Miami Sylvester Comprehensive Cancer Center, Bankhead Coley Cancer Research program (7BC10), Flight Attendant Medical Research Institute, and NIH R21CA191668 (to GW) and 1R56AG061911 (to CW and CHV). Keywords: Triple negative breast cancer, Vitamin C, BET inhibitor, Combination therapy, Histone acetylation, HDAC1

In this paper, we have developed a new method of accurate detection of retinal blood vessels based on a deep convolutional neural network (CNN) model. This method plays an important role in the observation of many eye diseases. Retinal Images have many issues that make the process of vessels segmentation very hard. We treat each issue of the retina image with the greatest observation to obtain a well-segmented image. The first step is to apply a pre-processing method based on fuzzy logic and image processing tactics. In a second step, in order to generate the segmented images, we propose a strided encoder-decoder CNN model. This network is trained and optimized using the Dice Loss function that supports the class imbalance problem that is in the database. The proposed model has a U-Net shape, but it is deeper and the pooling layers are replaced with strided convolutional layers in the encoder. This modification allows for a more precise segmentation of vessels and accelerates the training process. The last step is post-processing for removing the noisy pixels as well as the shadow of the optic disc. The performance of the proposed method was evaluated on DRIVE and STARE databases. The proposed method gives a sensitivity of 0.802 and 0.801 respectively on DRIVE and STARE, with an accuracy of 0.959 and 0.961 respectively. We focused on sensitivity and accuracy measurements that represent the accuracy of the model, especially tiny vessels. According to the results, the model outperforms many other proposed methods, especially in the above-mentioned measures.

This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis. Many eye diseases often lead to blindness in the absence of proper clinical diagnosis and medical treatment. For example, diabetic retinopathy (DR) is one such disease in which the retinal blood vessels of human eyes are damaged. The ophthalmologists diagnose DR based on their professional knowledge, that is labor intensive. With the advances in image processing and artificial intelligence, computer vision-based techniques have been applied rapidly and widely in the field of medical images analysis and are becoming a better way to advance ophthalmology in practice. Such approaches utilize accurate visual analysis to identify the abnormality of blood vessels with improved performance over manual procedures. More recently, machine learning, in particular, deep learning, has been successfully implemented in this area. In this paper, we focus on recent advances in deep learning methods for retinal image analysis. We review the related publications since 1982, which include more than 80 papers for retinal vessels detections in the research scope spanning from segmentation to classification. Although deep learning has been successfully implemented in other areas, we found only 17 papers so far focus on retinal blood vessel segmentation. This paper characterizes each deep learning based segmentation method as described in the literature. Analyzing along with the limitations and advantages of each method. In the end, we offer some recommendations for future improvement for retinal image analysis.

Objective: This study aims to investigate the effect of dexmedetomidine on perioperative stress response and immune function in patients with tumors. Methods: Sixty patients who underwent selective radical gastrectomy for cancer were randomly divided into 3 groups: remifentanil group (group R), dexmedetomidine group (group D), and sufentanil group (group S). Remifentanil, dexmedetomidine, and sufentanil were used as general anesthetics. Endotracheal intubation and mechanical ventilation were performed after the spontaneous respiration disappeared. Then, the data were recorded, and blood samples were collected at all time points. Results: The heart rate significantly increased ( P < 0.05) at T1 in group S, and both heart rate and mean arterial pressure significantly increased ( P < 0.05) in group R when compared to group D. The heart rate significantly increased ( P < 0.05) at T2 in group S and group R. Furthermore, the heart rate significantly increased ( P < 0.05) at T3 and T4 in group S and group R. Intra-group comparison: The heart rate at T1–T4 and mean arterial pressure at T1–T4 significantly increased ( P < 0.05) in group S, and the heart rate at T1 and T4, and mean arterial pressure at T2–T4 significantly increased ( P < 0.05) in group R when compared to T0. The serum IL-6, IFN-γ, and β-EP significantly increased ( P < 0.05) at T0’ in group S and group R when compared to group D. Blood glucose, and serum IL-10, IFN-γ, and β-EP significantly increased ( P < 0.05), while IL-18 significantly decreased ( P < 0.05) at T1’ in group S and group R. Conclusion: Continuous infusion of dexmedetomidine in combination with the inhalation of sevoflurane is superior to sevoflurane + remifentanil or sufentanil in patients undergoing tumor surgery.

An automatic retinal vessel segmentation method is proposed for quick and accurate segmentation of eye vessels. Such a method would play a vital role in analyzing many eye diseases. A retinal fundus image contains varying low contrasts, which undermine the performance of the segmentation process. Independent component analysis (ICA) is largely used for noise removal and consists of two architectures, designated as ICA1 and ICA2. We have validated both ICA architectures on retinal color fundus images and selected the one that provides improved contrast values. For retinal fundus, ICA2 architecture performed better than ICA1 by virtue of being more effective in compensating the low contrast values. Experiments conducted here validated the improvements over previously reported state-of-the-art methods. The impact of proposed segmentation model was assessed on publicly available databases like DRIVE and STARE. In case of the DRIVE database, the sensitivity increased 3% by (from 72% to 75%) while maintaining a segmentation accuracy of around 96%.

The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves.

Background: There is an increasing interest in the role of astrocytes contributing to the intrinsic bioremediation of ischemic brain injury. The purpose of this study was to disclose the effects and mechanism of midazolam (MDZ) on the proliferation and apoptosis of astrocytes under oxygen glucose deprivation (OGD) condition. Methods: The astrocytes were assigned randomly into four groups: control group, OGD group, OGD+MDZ group, and OGD+MDZ+IL-6 group. The astrocytes were treated with MDZ at dose of 10 μmol/L in OGD+MDZ group. And in OGD+MDZ+IL-6 group, the astrocytes were treated with MDZ at dose of 10μmol/L and IL-6 at dose of 50 ng/mL. MTT assay was used to assess cell proliferation, and cell apoptosis was analyzed by TUNEL apoptosis assay kit and flow cytometry. Furthermore, the expression of JAK2, p-JAK2, STAT3, p-STAT3, Bcl-2, Bax and Caspase-3 proteins were determined by western blotting assay. Results: Astrocytes proliferation was decreased obviously in OGD group, while MDZ could increase astrocytes proliferation under OGD condition. Moreover, OGD could induce apoptosis in astrocytes and MDZ could play an anti-apoptotic role. However, IL-6, a JAK2 activator, could attenuate cell proliferation and anti-apoptotic effects of MDZ in astrocytes. In addition, the expression of Bcl-2 protein in MDZ group increased markedly, while the JAK2/STAT3 signal proteins, Bax and Caspase-3 proteins decreased relative to OGD group. But IL-6 could counteract the anti-apoptotic effects of MDZ. Conclusion: Midazolam has protective effects on the proliferation and apoptosis of astrocytes via JAK2/STAT3 signal pathway in vitro. We firstly disclose the beneficial roles of midazolam in astrocytes under ischemic condition, which may be a rational treatment selection for ischemic cerebral protection.

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

Y chromosomal STRs show sufficient variability among individduals in a population and a high degree of geographical differentiation, such that their polymorphic character makes them especially suited for population genetic studies. To investígate the polymorphism of a set of 17 Y-STR loci in northern China, we genotyped the 17 Y chromosomal STR loci in a population sample of 377 unrelated males from eight ethnic populations in northern China. We calculated the haplotype frequencies, Rst value and carried out the analysis of molecular variance (AMOVA). We then drew the multidimensional scaling analysis (MDS) plot and phylogenetic tree based on the Rst value. All populations showed a high level of haplotype diversity, with low inter-population variance as measured by an analysis of molecular variance. However, the genetic distances were significant when the eight populations were compared to other populations. By MDS and the phylogenetic tree, we found that the eight populations had a close relationship and Xibo had a northeast origination.

This paper focuses on developing diagonal gradient-type methods that employ accumulative approach in multistep diagonal updating to determine a better Hessian approximation in each step. The interpolating curve is used to derive a generalization of the weak secant equation, which will carry the information of the local Hessian. The new parameterization of the interpolating curve in variable space is obtained by utilizing accumulative approach via a norm weighting defined by two positive definite weighting matrices. We also note that the storage needed for all computation of the proposed method is just O(n). Numerical results show that the proposed algorithm is efficient and superior by comparison with some other gradient-type methods.

Background: New hemoglobin (Hb) variants are constantly being updated as assays are developed and the testing population expands. Here, we report a novel Hb variant, named Hb Guigang. Methods: Hemoglobin (Hb) analysis was analyzed by capillary electrophoresis (CE) and high-performance liquid chromatography (HPLC). Glycated hemoglobin was performed by CE and HPLC. Routine genetic analysis was done with Gap-PCR and PCR-reverse dot-blot hybridization. The hemoglobin variant was identified by Sanger sequencing. Results: CE of three cases showed the presence of Hb variants in Zone 5 and Zone 12, respectively. HPLC indicated an elevated P3 peak, suggesting the possible presence of the Hb variant. Hb A1c was measured by CE and HPLC, and the results were 6.7% and 4.76%, respectively. Sanger sequencing confirmed an AAG˃AAT mutation at codon 90 of the HBA1 gene. This mutation was reported for the first time, and we named it Hb Guigang based on the proband’s place of residence. Conclusions: Hb Guigang with normal hematological parameters was separated and quantified by CE, whereas HPLC suggested that Hb Guigang co-eluted with the P3 peaks and could not be quantified. [ABSTRACT FROM AUTHOR]