Search

In vision domain, large-scale natural datasets typically exhibit long-tailed distribution which has large class imbalance between head and tail classes. This distribution poses difficulty in learning good representations for tail classes. Recent developments have shown good long-tailed model can be learnt by decoupling the training into representation learning and classifier balancing. However, these works pay insufficient consideration on the long-tailed effect on representation learning. In this work, we propose interpolative centroid contrastive learning (ICCL) to improve long-tailed representation learning. ICCL interpolates two images from a class-agnostic sampler and a class-aware sampler, and trains the model such that the representation of the interpolative image can be used to retrieve the centroids for both source classes. We demonstrate the effectiveness of our approach on multiple long-tailed image classification benchmarks. Our result shows a significant accuracy gain of 2.8% on the iNaturalist 2018 dataset with a real-world long-tailed distribution., Comment: Add in acknowledgment

Junior high school students are at a critical stage of adolescent development. These children are emotionally sensitive and prone to mental health problems. A survey of the mental health status of junior high school pupils found that the detection rate of learning anxiety was highest in junior high school pupils and increased with grade level. Therefore, it is necessary to investigate the causes and influencing factors of learning anxiety in depth. This study analyzes and compares the existing literature on the effects of interpersonal relationships on learning anxiety in junior high school students. Results show that normal parent-child relationships, supportive teacher-student relationships, and peer support were all negatively associated with learning anxiety. In contrast, unhealthy interpersonal relationships will increase students learning anxiety level. This paper explores how parent-child, teacher-student, and peer relationships influence learning anxiety among junior high school students, reviewing and collating data to derive both a dominant-subordinate and an integrative perspective. Overall, interpersonal relationships affect learning anxiety in junior high students in different ways and to various degrees, and interpersonal relationships affect the generation and mitigation of learning anxiety in junior high students to some extent.

Carrying out gender equality education in junior middle schools is one of the significant objectives and tasks in the Development of Chinese Women (2021-2030). The adolescent stage is a crucial period for gender equality education. It is significant for teenagers to implement and promote gender equality education in junior high school. This paper starts with the current situation of gender equality education in junior middle schools in our country. It analyzes the existing problems of gender education from four aspects: school education policy, curriculum setting, teachers' gender education literacy, and classroom teaching. Based on the problem analysis, this paper concludes that schools and teachers should start by establishing gender equality consciousness, starting from campus activities, curriculum system design, and classroom teaching activities, and put forward corresponding suggestions and countermeasures.

Inflammation and immune dysregulation are involved in the pathogenesis of diabetic macular edema (DME). The progressive increase of neutrophils in peripheral blood can lead to the increase of the number of neutrophils in the retina, thus leading to the sustained damage of the retinal vascular system and the destruction of the blood retinal barrier (BRB); lymphocytes play a protective role in vascular diseases caused by type 2 diabetes mellitus (T2DM). The purpose of this study was to study the relationship between the changes of leukocytes and their classification in peripheral blood and the occurrence and progression of DME in patients with T2DM.A retrospective analysis was made on 81 patients with T2DM with DME (DME group) hospitalized in our hospital from January 2019 to December 2020. According to the morphological characteristics of macular edema in optical coherence tomography (OCT), they were divided into early DME group (n=33) and late DME group (n=48); 33 patients with diabetes retinopathy (DR) but without DME matched in age and course of disease served as the control group (NO-DME group). The clinical parameters assessed included eye examination, OCT results, WBCs and subtypes, blood glucose, and glycosylated hemoglobin.Compared with NO-DME group (n=33), Neutrophils% in DME group (n=81) was higher (57.37±9.52 vs. 63.27±7.85; P=0.001); Monocyte% (7.63±1.77 vs. 6.88±1.83; P=0.047) and lymphocyte% (30.35±9.51 vs. 27.26±6.59; P=0.032) were decreased. The optimal model was obtained with R 4.0.5 software. With other relevant variables being the same, females had a significantly increased risk of DME (b=1.273, P=0.015), %neutrophils was significantly associated with increased risk of DME (b=0.152, P=0.0006), and %lymphocytes was significantly associated with a reduced risk of DME (b=-0.027, P=0.179). However, in the early and late DME groups, no significant differences in biological markers were found, and a high-quality model was not obtained.In this preliminary study, %neutrophils is associated with increased risk of DME, whereas %lymphocytes is associated with a reduced risk of DME.

Herein, we combined a derivative of cisplatin (CP) and the chemosensitizer lonidamine (LND) to design an amphiphilic prodrug, in which the ratio of LND to cisplatin was fixed at 2:1. Diaminedichlorodihydroxyplatinum (DH-CP) is a hydrophilic cisplatin derivative. Due to its appropriate amphiphilicity, this prodrug could self-assemble into stable nanoparticles (denoted as LNP-NPs). Under the action of excessive glutathione (GSH) in tumor cells, DH-CP could be reduced to cytotoxic cisplatin. In addition, the released LND could inhibit the metabolic process of tumor cells, and improving the sensitivity of tumor cells to cisplatin. In vitro studies demonstrated that LNP-NPs displayed significantly cytotoxicity on breast cancer cells, and the cell viability after co-incubation for 48 h (CP 16 μg/mL) were 18.77% (MCF-7) and 20.01% (EMT6), respectively. LNP-NPs could also significantly inhibit the growth of MCF-7 tumor-like spheroids, which were realized through the high coordination and cooperation between CP and LND. Therefore, the carrier-free drug delivery system based on LND and DH-CP is expected to achieve a good synergistic anti-tumor effect.

The k nearest neighbor (KNN) classifier is one of the well-known instance-based classifiers. Nevertheless, the low efficiency in both running time and memory usage is a great challenge in the KNN classifier and its improvements due to noise and redundant samples. Although hybrid instance reduction approaches have been postulated as a good solution, they have the following issues: (a) adopted edition methods in existing hybrid instance reduction approaches are susceptible to harmful samples around the tested sample; (b) existing hybrid instance reduction approaches retain many internal samples, which contributes little to the classification accuracy and (or) leading to the low reduction rate; (c) existing hybrid instance reduction approaches rely on too many parameters. The chief contributions of this article are that (a) a novel heuristic hybrid instance reduction approach based on adaptive relative distance and k-means clustering (HIRRDKM) is proposed against the above issues; (b) a novel concept, i.e., the adaptive relative distance, is first proposed and calculated for each sample; (c) a novel edition method based on adaptive relative distance (EMARD) in HIRRDKM is second proposed to filter out harmful samples; (d) a novel condensing method based on adaptive relative distance and k-means clustering (CMRDKM) in HIRRDKM is third proposed to obtain condensed borderline samples from the training set without harmful samples. Experiments have proved that (a) HIRRDKM outperforms 6 state-of-the-art hybrid instance reduction methods on real data sets from various fields in weighing reduction rate and classification accuracy of KNN-based classifiers; (b) the running time of HIRRDKM is competitive.

Background The discrepancy between the results of cytogenetics and the results of chromosome microarray analysis (CMA) has often led to confusion over genetic counselling for prenatal diagnosis. Case presentation The prenatal ultrasound results of a congenital heart defect (CHD) foetus displayed an apartial endocardial pad defect and permanently dilated coronary sinus and left superior vena cava at 21 weeks of gestation. Cytogenetic analysis, CMA, fluorescent in situ hybridization (FISH) and multiplex ligation-dependent probe amplification (MLPA) with foetal cord blood samples were used to detect the genetic aetiology. Routine G-binding cytogenetic analysis showed normal karyotypes in both the foetus’ and parents’ blood samples. CMA results demonstrated that there were 53.973-Mb recurrent CNVs at Xp22.33-p11.22, as confirmed by MLPA assay. Conclusions Herein, we described the CNV of six duplications at Xp22.33-p11.22 and the 53.973 Mb duplication CNV that was not found in foetal cord blood samples by conventional cytogenetic methods, and it was confirmed by CMA and MLPA. Our novel findings will provide helpful information for prenatal diagnosis and genetic counselling for foetal CHDs.

The balanced iterative reducing and clustering using hierarchies (BIRCH) method was adopted to optimize the results of the resting-state functional magnetic resonance imaging (RS-fMRI) to analyze the changes in the brain function of patients with chronic pain accompanied by poor emotion or abnormal sleep quality in this study, so as to provide data support for the prevention and treatment of clinical chronic pain with poor emotion or sleep quality. 159 patients with chronic pain who visited the hospital were selected as the research objects, and they were grouped according to the presence or absence of abnormalities in emotion and sleep. The patients without poor emotion and sleep quality were set as the control group (60 cases), and the patients with the above symptoms were defined in the observation group (90 cases). The brain function was detected by RS-fMRI technology based on the BIRCH algorithm. The results showed that the rand index (RI), adjustment of RI (ARI), and Fowlkes–Mallows index (FMI) results in the k-means, flow cytometry (FCM), and BIRCH algorithms were 0.82, 0.71, and 0.88, respectively. The scores of Hamilton Depression Scale (HAHD), Hamilton Anxiety Scale (HAMA), and Pittsburgh Sleep Quality Index (PSQI) were 7.26 ± 3.95, 7.94 ± 3.15, and 8.03 ± 4.67 in the observation group and 4.03 ± 1.95, 5.13 ± 2.35, and 4.43 ± 2.07 in the control group; the higher proportion of RS-fMRI was with abnormal brain signal connections. A score of 7 or more meant that the number of brain abnormalities was more than 90% and that of less than 7 was less than 40%, showing a statistically obvious difference in contrast P < 0.05 . Therefore, the BIRCH clustering algorithm showed reliable value in the optimization of RS-fMRI images, and RS-fMRI showed high application value in evaluating the emotion and sleep quality of patients with chronic pain.

Electrocatalytic formation of C–N bonds was achieved through the electrolysis of CO2 and NH3 over Cu catalysts. A combined analytical and spectroscopic approach gave insights into the reaction mechanism leading to formamide and acetamide products.

Summary The renewable-energy-driven conversion of CO2 is an important means of generating carbon-based fuels and chemicals to power a sustainable society. While most CO2 reduction (CO2R) is carried out in alkaline electrolyte, working in such conditions often leads to spontaneous carbonate formation and, consequently, a low energy balance and CO2 utilization rate. Alternatively, operating in acid alleviates these issues, but achieving selective CO2R in the presence of high proton concentrations has proven to be a challenge over the years. Recently, a host of works have emerged that have demonstrated both a proof of concept and initial design principles for CO2R in acid. As such, this perspective will cover the key initial findings that steer catalysis towards CO2R. After an overview of successful systems, we turn to the future in examining what key questions remain and steps can be taken in this emerging area to bring CO2R in acid toward practical feasibility.

Objective: This study aims to investigate the effects of leptin on human chondrocytes in vitro from the autophagy regulated by the mammalian target of rapamycin(mTOR)signaling pathway. Methods: Human articular chondrocyteswere isolated and cultured. Chondrocytes were identified by Toluidine blue staining and Col II immunocytochemistry staining. Chondrocytes were assigned into the blank group, Interleukin-1β (IL-1β) group, leptin group, leptin+IL-1β group, leptin+leptin antagonistgroup, and leptin+leptin antagonist+IL-1β group. Cell proliferation and apoptosis were detected byCell Counting Kit-8 (CCK8) and flow cytometry respectively. Cell autophagy was detected by Monodansylcadaverine(MDC) staining and mRFP-GFP-LC3fluorescence microscopy was utilized to evaluate autophagy rates. Results: The result of CCK8 assay presented the cell proliferation rate of had no difference between the leptin group and the blank group, and IL-1β group was lower, leptin+IL-1β group was in the middle. In flow cytometry, the apoptosis rate of the leptin+IL-1β group was the lower than IL-1β group. MDC-positive staining cells and the level of autophagy showed despite the autophagy rate was increased in IL-1β group, but in leptin+IL-1β group, the autophagy rate was decreased. Quantitative real-time PCR(QRT-PCR) and Western blotting showed the mRNA expressions of Atg5 and Atg7 and the protein expressions of LC3B were corresponded with the level of autophagy. But the expressions of mTOR protein was opposite changed. Conclusions: IL-1β could inhibit human chondrocyte proliferation and up-regulation of the level of autophagy. Leptin could attenuate the effect of IL-1β on chondrocytes and down-regulation the autophagy, the mechanism may be related to the mTOR signaling pathway.

Volatile compounds in nectar have various functions, but we still know little about the distribution and functions of scented floral nectar. Here, we report functions of scented nectar in Fritillaria camtschatcensis. In Japan, F. camtschatcensis comprises two varieties, var. keisukei (hereafter, alpine type) and var. camtschatcensis (hereafter, lowland type), both of which secrete a large amount of scented nectar and attract flies as pollinators. We investigated sugar concentration of the nectar using high-performance liquid chromatography (HPLC) and studied the volatile chemical composition of the nectar using gas chromatography-mass spectrometry (GC-MS). We then conducted a bioassay using a synthetic mixture of components of the nectar scent of the lowland type to test if the nectar scent contributes to pollinator attraction. The HPLC analysis showed that substantial amounts of glucose and fructose occur in the nectar of the alpine type. The nectar of the lowland type also contained glucose, albeit at low concentration. The volatile compounds in the nectars were substantially different between the two types: the volatiles of the lowland type consisted of four aliphatic acids, whereas acetophenone was the main compound in the alpine type. The bioassay revealed that the nectar scent of the lowland type is sufficient to attract flies, including the pollinator species. This study showed that the nectar of F. camtschatcensis functions as both a reward and attractant for the pollinators, and indicates that flower nectar is much more diverse in terms of chemical composition and function than commonly thought.

Background Antitumor T cell immunotherapy as a novel cancer therapeutic strategy has shown enormous promise. However, the tumor microenvironment (TME) is characterized by the low immunogenicity, hypoxia, and immunosuppressive condition that dramatically limit effective T cell immunotherapy. Thus, an ideal immunotherapy strategy that is capable of reversing the immunosuppressive TME is highly imperative. Results In this article, we reported that Fe-doped and doxorubicin (DOX) loaded HA@Cu2−XS-PEG (PHCN) nanomaterials were rationally designed as targeted Fe-PHCN@DOX nano-nuclear-reactors, which evoked persistent T cell immune response together with anti-PD-L1 nanobodies. It was confirmed that nano-nuclear-reactors displayed strong nanocatalytic effect for effective antitumor effects. Consequently, they maximized the immunogenic cell death (ICD) effect for antigen presentation and then stimulated T cell activation. In addition, Fe-PHCN@DOX could reprogram M2-phenotype tumor-associated macrophages (TAMs) into M1-phenotype TAMs by relieving tumor hypoxia. Meanwhile, blockade of the anti-PD-L1 nanobody promoted T cell activation through targeting the PD-1/PD-L1 immunosuppressive pathway. Notably, in vivo tumor therapy verified that this nano-nuclear-reactor could be used as an excellent immunotherapy nanoplatform for tumor eradication and metastasis prevention with nanobody. Conclusions Our findings demonstrated that nano-nuclear-reactors in combination with nanobody could evoke persistent T cell immune activation, suggesting them potential as a promising immunotherapy option for reversing immunosuppressive immune-cold tumors. Graphical Abstract

Nonintrusive industrial load identification can accurately acquire the operation data of each load in the plant, which is the benefit of intelligent power management. The identification method of the industrial load is complicated and difficult to be realized due to the difficulty in collecting transient data for modeling, and high-precision measuring equipment is required. Aiming at this situation, the article proposes a nonintrusive industrial load identification method using a random forest algorithm and steady-state waveform. Firstly, by monitoring the change of the industrial load power state, when the load changes and becomes stable, the steady-state waveform is extracted. Due to different electrical characteristics of industrial loads, the current waveform of loads is different to some extent. We can construct characteristic data for each industrial load to construct its own current steady-state waveform. Then, using the high-dimensional data of the steady-state waveform as the sample data, the bootstrap sampling method and the CART algorithm in the random forest algorithm are used to generate multiple decision trees. Finally, the industrial load types are identified by voting multiple decision trees. The actual operating load data of a factory are used as the sample data in the simulation, and the effectiveness and rapidity of the proposed identification algorithm are verified by the combined load method simulation comparison. The simulation results show that the accuracy of the proposed identification algorithm is more than 99%, the identification time is 3.36 s, which is much higher than that of other methods, and the operation time is less than that of other methods. Therefore, the proposed identification algorithm can effectively realize the nonintrusive industrial load identification.

To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC). A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model. Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75–0.93) and 0.79 (95% CI: 0.59–0.99) in the primary cohort and validation cohort, respectively. Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment. • Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.

Hierarchical clustering is a common unsupervised learning technique that is used to discover potential relationships in data sets. Despite the conciseness and interpretability, hierarchical clustering algorithms still face some challenges such as inaccuracy, time-consuming, and difficulty in choosing merging strategies. To overcome these limitations, we propose a novel Hierarchical Clustering algorithm with a Merging strategy based on Shared Subordinates (HCMSS), which defines new concepts of the local core representative and the shared subordinate belonging to multiple representatives. First, the state-of-the-art natural neighbor (NaN) is introduced to compute the local neighborhood and the local density of each data point. Next, a sharing-based local core searching algorithm (SLORE) is proposed to find local core points and divide the input data set into numerous initial small clusters. Lastly, these small clusters are merged hierarchically and form the final clustering result. We creatively split the merging process into two sub-steps: first, pre-connecting small clusters according to a shared-subordinates-based indicator that measures the stickiness between clusters; second, merging the pre-connected intermediate clusters and the remaining unconnected small clusters in a classical hierarchical way. Experiments on 8 synthetic and 8 real-world data sets demonstrate that HCMSS can effectively improve the clustering accuracy and is less time-consuming than 2 state-of-the-art benchmarks.

Congenital diaphragmatic hernia is a congenital fetal disease, which mainly causes pulmonary hypoplasia and pulmonary hypertension. Effective early prenatal diagnosis can detect and predict the prognosis of congenital diaphragmatic hernia in infants, thus provide a reference for prenatal counseling, early intervention, and potential choices for the child's family. Ultrasound and magnetic resonance imaging are the most commonly used methods for non-invasive examination of the fetus. This paper discusses evaluation parameters based on these two imaging modalities.

This investigation aims to determine the antidepressant role of Xingpijieyu formula (XPJYF) mediated via gut microbiota (GM)-brain axis.We collected fecal microbiota from patients with depressive disorder (DD) and cultured microbiota in vitro. Some of microbiota were transplanted into germ-free rats with the intragastric administration of XPJYF grain at the dose of 1.533 g/kg/day. The behaviors were studied by forced swimming test, open field test, sucrose preference test, and body weight. Products of hypothalamus-pituitary-adrenocortical (HPA) axis, neurotransmitter, and serum cytokines were investigated by enzyme linked immunosorbent assay. Glial fibrillary acidic protein (GFAP), a biomarker of astrocyte, was quantified using immunofluorescence. Microbiota culturing in vitro after XPJYF treatment was analyze by 16 s RNA sequencing technology. We used lipopolysaccharide (LPS) to mimic activated rat primary astrocyte in vitro. Brain-derived neurotrophic factor (BDNF), cytokines, and oxidative stress factors were determined by western blotting, and glycometabolism in astrocyte was investigated by 2-deoxy-D-glucose (2-DG) uptake, adenosine triphosphate (ATP), and glucose-1-phosphate (G1P) kits.Microbiota composition during 8 mg/ml of XPJYF (H12-8) for 12 h showed the more consistency. Lactococcus is enriched in DD-derived microbiota composition, and Biffdobacterium and Lactobacillus in H12-8 group. GLUCOSE1PMETAB-PWY and PWY-7328 of which biofunctions were dominantly encoded by Biffdobacterium were the top two of altered pathways. XPJYF improved behaviors and repressed astrocyte activation in depression rats. XPJYF elevated 2-DG uptake, ATP, glucose-1-phosphate, and brain-derived neurotrophic factor (BDNF), and inhibited cytokines and oxidative stress in LPS-induced astrocyte.XPJYF treatment targets inflammation, activation, and glycometabolim in astrocyte via gut microbiota modulation, thereby improve animal behaviors, HPA axis dysfunction, and neurotransmitter synthesis in depression rats.

The electrochemical conversion of nitrate (NO3-) to ammonia (NH3) is an emerging alternative to valorize this aqueous pollutant to an essential chemical feedstock and potential fuel. Underpinning the maturation of the field and eventual viability of this technology is the discovery of efficient electrocatalysts, coupled with fundamental insights into the reaction mechanism. Until now, fluorinated materials have not yet been explored in this direction. In this work, we present the first fluorinated catalyst used for electrochemical NO3- reduction to NH3. A new copper-based oxyfluoride, Cu3Al2OF10, prepared through a facile coprecipitation and annealing of the corresponding hydrated fluoride r-Cu3Al2F12(H2O)12, was found to be exceptionally active, attaining a NH3 Faradaic Efficiency (FE) of up to 57% for the 8-electron NO3- to NH3 pathway (-0.4 VRHE) with a mass activity of up to 1220 A.g-1 at -0.6 VRHE, the highest yet recorded. Additionally, Cu3Al2OF10 continually produced NH3 for 2.5 days while maintaining a reasonable FE (55%). Finally, electroanalytical and operando spectroscopic investigations revealed a reversible transition to a phase entailing Cu nanoparticles embedded within the amorphous oxyfluoride matrix that was predominantly responsible for the catalyst’s activity. Overall, this work stands to open avenues for transition metal fluoride materials within the field nitrogen-based electrocatalysis.

After the identification of specific epidermal growth factor receptor (EGFR)-activating mutations as one of the most common oncogenic driver mutations in non-small cell lung cancer (NSCLC), several EGFR-tyrosine kinase inhibitors (EGFR-TKIs) with different clinical efficacies have been approved by various health authorities in the last two decades in targeting NSCLC harboring specific EGFR-activating mutations. However, most patients whose tumor initially responded to the first-generation EGFR-TKI developed acquired resistance. In this study, we developed a novel combination strategy, "antiADAM17 antibody A9(B8) + EGFR-TKIs", to enhance the efficacy of EGFR-TKIs. The addition of A9(B8) was shown to restore the effectiveness of erlotinib and overcome acquired resistance. We found that when A9(B8) antibody was treated with erlotinib or gefitinib, the combination treatment synergistically increased apoptosis in an NSCLC cell line and inhibited tumor growth in vivo. Interestingly, the addition of A9(B8) could only reduce the survival of the erlotinib-resistant NSCLC cell line and inhibit the growth of erlotinib-resistant tumors in vivo but not gefitinib-resistant cells. Furthermore, we revealed that A9(B8) overcame erlotinib resistance through the FOXO3a/FOXM1 axis and arrested the cell cycle at the G

Developing techniques for the machine learning of a classifier from class-imbalanced data presents an important challenge. Among the existing methods for addressing this problem, SMOTE has been successful, has received great praise, and features an extensive range of practical applications. In this paper, we focus on SMOTE and its extensions, aiming to solve the most challenging issues, namely, the choice of the parameter k and the determination of the neighbor number of each sample. Hence, a synthetic minority oversampling technique with natural neighbors (NaNSMOTE) is proposed. In NaNSMOTE, the random difference between a selected base sample and one of its natural neighbors is used to generate synthetic samples. The main advantages of NaNSMOTE are that (a) it has an adaptive k value related to the data complexity; (b) samples of class centers have more neighbors to improve the generalization of synthetic samples, while border samples have fewer neighbors to reduce the error of synthetic samples; and (c) it can remove outliers. The effectiveness of NaNSMOTE is proven by comparing it with SMOTE and extended versions of SMOTE on real data sets .

The energy output and energy conversion efficiency of MTPV system are relatively low due to the energy loss. In order to improve the energy output of micro-thermophotovoltaic (MTPV) system, the internal straight and spiral fins are introduced into the micro combustor. The impact of hydrogen mass flow rate, equivalence ratio, and materials on the thermal performance are investigated. The increase of hydrogen mass flow rate brings higher average outer wall temperature, but the temperature difference also increases and the temperature uniformity becomes worse. The equivalence ratio of 1 is suggested to obtain higher average outer wall temperature and better temperature uniformity. The materials with higher thermal conductivity can obtain better thermal performance. Meanwhile, the higher thermal conductivity can also reduce the impact of introduction of internal fins.

ObjectiveTo explore the predictive capacity of umbilical artery (UA) velocities at 37 weeks of gestation in identifying fetal growth restriction (FGR).DesignCross-sectional study.Setting and participantsWe retrospectively recruited 569 fetuses in the study. Thirty-nine FGR infants and 57 small-for-gestational-age (SGA) infants with normal UA Doppler at 37 weeks, as the study groups and 473 adequate-for-gestational-age (AGA) infants as a control group in a tertiary referral centre.MethodsAll the parameters of UA velocities, including the UA end-diastolic velocity (UA-EDV), UA peak systolic velocity (UA-PSV), UA mean diastolic velocity (UA-MDV) and UA time-averaged maximum velocity (UA-TAMXV), and UA Doppler were measured at approximately 37 weeks of gestation.ResultsAmong the FGR, SGA and AGA groups, the UA-MDV, UA-TAMXV, UA-PSV and UA-EDV decreased with the loss of fetal weight. Multivariate logistic regression analyses showed that the UA-TAMXV was an independent predicting factor of FGR. It had a moderate predictive value for FGR. The area under the receiver operating characteristic curve was 0.82 (95% CI: 0.79 to 0.85).ConclusionsThe UA velocities decreased with the loss of fetal weight among the FGR, SGA and AGA groups and the UA-TAMXV was independently predictive of FGR. The results suggest that the UA-TAMXV might be a new parameter to predict FGR prior to delivery.

The formation of C-S bonds is an important step in the synthesis of pharmaceutical, biological, and chemical products. A very attractive green route to C-S bond containing species would be one driven through electrocatalysis using abundant small molecule precursors but examples within this context are largely absent from the literature. To this end, this work demonstrates the use of CO2 and SO32- as cheap building blocks that couple on the surface Cu-based heterogeneous catalysts to form hydroxymethanesulfonate, sulfoacetate and methane sulfonate for the first time, with Faradaic efficiencies of up to 9.5%. A combination of operando measurements and computational modelling reveal that *CHOH formed on metallic Cu is a key electrophilic intermediate that is nucleophilically attacked by SO32- in the principal C-S bond forming step. In all, the proof-of-concept for electrocatalytic C-S bond formation and mechanistic insights gained stand to substantially broaden the scope of the emerging field of electrosynthesis.

OBJECTIVE Twin-twin transfusion syndrome (TTTS) causes perinatal mortality and morbidity in monochorionic twins. The early recognition of and interventional therapy for TTTS is associated with a more favorable overall prognosis. However, the prediction by the use of ultrasound in the first trimester has relatively poor sensitivity and specificity. This study aimed to identify metabolic biomarkers to aid in ultrasound screening of TTTS. METHODS Maternal plasma was prospectively collected between 11 and 15 weeks of gestation in apparently uncomplicated monochorionic-diamniotic twin pregnancies. This cohort was divided into: (i) patients who were subsequently diagnosed with TTTS by using ultrasound; (ii) uncomplicated matched controls. Metabolome was profiled by using gas chromatography-mass spectrometry. RESULTS The levels of fatty acids, organic acids, oxaloacetic acid, and beta-alanine were significantly lower in the TTTS maternal plasma at 11-15 weeks of gestation, and methionine and glycine were also higher (p

The investigation aims to evaluate the potential effect of Shugan Granule (SGKL) on the gut, brain, and behaviors in rats exposed to chronic restraint stress (CRS).The fecal microbiota and metabolite changes were studied in rats exposed to CRS and treated with SGKL (0.1 mg/kg/day). Depressive behaviors of these rats were determined through an open-field experiment, forced swimming test, sucrose preference, and weighing. Moreover, LPS-stimulated microglia and CRS-stimulated rats were treated with SGKL to investigate the regulation between SGKL and the PI3K/Akt/pathway, which is inhibited by LY294002, a PI3K inhibitor.(i) SGKL improved the altered behaviors in CRS-stimulated rats; (ii) SGKL ameliorated the CRS-induced neuronal degeneration and tangled nerve fiber and also contributed to the recovery of intestinal barrier injury in these rats; (iii) SGKL inhibited the hippocampus elevations of TNF-α, IL-1β, and IL-6 in response to CRS modeling; (iv) based on the principal coordinates analysis (PCoA), SGKL altered α-diversity indices and shifted β-diversity in CRS-stimulated rats; (v) at the genus level, SGKL decreased the CRS-enhanced abundance of Bacteroides; (vi) Butyricimonas and Candidatus Arthromitus were enriched in SGKL-treated rats; (vii) altered gut microbiota and metabolites were correlated with behaviors, inflammation, and PI3K/Akt/mTOR pathway; (viii) SGKL increased the LPS-decreased phosphorylation of the PI3K/Akt/mTOR pathway in microglia and inhibited the LPS-induced microglial activation; (ix) PI3K/Akt/mTOR pathway inactivation reversed the SGKL effects in CRS rats.SGKL targets the PI3K/Akt/mTOR pathway by altering gut microbiota and metabolites, which ameliorates altered behavior and inflammation in the hippocampus.

This study was conducted to construct a multifunctional nanodrug delivery system (NDDS) to deplete glutathione (GSH) in tumor cells and amplify oxidative stress, enhancing the synergistic effect of chemotherapy and photodynamic therapy (PDT). l-Buthionine-sulfoximine (BSO) and chlorin e6 (Ce6) were loaded into mesoporous silicon nanoparticles (MSN), and then MSN were modified with oxidized hyaluronic acid (OHA) as a pore-blocking agent. Cisplatin (Pt(II)) was further loaded by a coordination reaction with carboxyl groups in OHA to yield a multifunctional NDDS (denoted as MSN@OHA-Ce6/BSO/Pt). The physicochemical properties and antitumor activity of the prepared nanoparticles were characterized in detail. In vitro and in vivo experiments demonstrated that OHA was shed from MSN@OHA-Ce6/BSO/Pt under acidic conditions in tumors, resulting in the release of free BSO, Ce6, and Pt(II). The released BSO could reduce intracellular GSH expression by 48.8 %, effectively enhancing the PDT effect of Ce6 and the chemotherapy effect of Pt(II). Finally, the tumor inhibitory rate (vs saline) reached 73.8 % ± 2.5 % for MSN@OHA-Ce6/BSO/Pt in A549/DDP tumor-bearing nude mice. Therefore, the multifunctional NDDS significantly enhanced the synergistic effect of PDT and chemotherapy.

Geological disasters damage highways and disrupt traffic, which reduces the service level of roads. We collected 15,435 geohazards in Guangxi Province. The geohazards within 1km of the highway buffer were selected to study its spatial distribution characteristics and risk assessment methods. Geohazard intensity index and sensitivity index are proposed to establish the highway geohazards assessment model. Based on the weights of evidence (WOE) method, the influencing factors of the sensitivity index and the weight of each factor are obtained. By analyzing the spatial distribution characteristics of geohazards, collapses, landslides, ground collapse and unstable slopes are identified as major geohazards along the highway. Based on the Getis-Ord Gi* statistic methods, the hotspot map of four disasters was drawn, and the intensity index along the highway was calculated. The relationship between slope, aspect, topographic relief, annual average rainfall, land use, lithology type and geohazards was analyzed. The sensitivity index was calculated using the WOE method. The intensity index and the sensitivity index were superimposed to obtain the geohazard index, and the geohazard risk of the highway is divided into five levels. The model accuracy-test based on the receiver operating characteristic (ROC) curve shows that Area Under Curve (AUC) equals 0.71, indicating high reliability for the risk assessment.

Background Rare chromosomal structural abnormalities, including ring chromosomes, often pose challenges to clinical genetic counselling. Results Here, we report a newborn with congenital heart disease and developmental delay who inherited ring chromosome 6 [46,XY,r(6)(p25q27)mat] from a phenotypically normal mother. Genotypes and phenotypes were analysed by molecular cytogenetic analysis, whole-exome sequencing and literature review. Conclusions Our study showed that the pathogenicity of the ring chromosome abnormality [r(6)(p25q27)] was mainly affected by chromosome imbalance, deletions of genes with haploinsufficiency, duplications of genes with triple sensitivity, parental inheritance of the imbalance and the imprinting status of the affected genes.

BACKGROUND: Ocular siderosis (OS) is a rare severe complication arising from retained iron-containing intraocular foreign bodies. In the young male patient reported in this paper, the intraretinal diffusion and distribution of siderotic particles before and after the removal of the foreign body were observed via fundus angiography (FA). CASE DESCRIPTION: A 33-year-old decoration worker was diagnosed as the absolute stage of right eye glaucoma in the local hospital and was treated with cataprolol and brinzolamide eye liquid to reduce intraocular pressure. The symptoms did not improve. He came to our hospital for treatment. He was finally diagnosed with “foreign body, siderosis, and secondary glaucoma in the right eye” based on eye examinations. According to the observation via fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), grade 2–3 vascular occlusion extended in a gradient along the foreign body, there was no retinal perfusion in the temporal retina, the siderotic particles were distributed along the artery, and inflammatory changes occurred in the optic nerve and retinal vein. He underwent pars plana vitrectomy, foreign body removal, retinal laser photocoagulation, silicone oil filling and pressure reducing valve implantation. Silicone oil was removed three months later. The postoperative visual acuity was not improved, the intraocular pressure was well controlled, and there was concurrent cataract, but it did not affect the fundus examination. At 3 and 10 months after operation, the fundus angiography showed that the particles distributed along the artery decreased, the inflammation of optic nerve and retinal vein subsided, and the laser spots in the non perfusion area were clear. CONCLUSIONS: The influence of iron ion on retinal vein and the distribution of iron ion along retinal artery can be seen macroscopically by FA before and after foreign body removal.

Trends such as increasing mobile device ownership and faster and more affordable internet speed have contributed to significant demands in media-based services on mobile devices. There has been an emphasis on content delivery networks to support media-based services. However, achieving high-performance content delivery in large-scale dynamic network environments is still operationally challenging, especially when we have to consider the diverse application requirements. One emerging research trend is to explore the use of machine learning techniques to enhance content delivery quality. in this article, we review and discuss existing state of the art machine learning-based approaches on enhancing content delivery performance. Discussions in this article will benefit readers and help identify future research agendas and opportunities.

The semi-supervised self-training method is one of the successful methodologies of semi-supervised classification and can train a classifier by exploiting both labeled data and unlabeled data. However, most of the self-training methods are limited by the distribution of initial labeled data, heavily rely on parameters and have the poor ability of prediction in the self-training process. To solve these problems, a novel self-training method based on density peaks and natural neighbors (STDPNaN) is proposed. In STDPNaN, an improved parameter-free density peaks clustering (DPCNaN) is firstly presented by introducing natural neighbors. The DPCNaN can reveal the real structure and distribution of data without any parameter, and then helps STDPNaN restore the real data space with the spherical or non-spherical distribution. Also, an ensemble classifier is employed to improve the predictive ability of STDPNaN in the self-training process. Intensive experiments show that (a) STDPNaN outperforms state-of-the-art methods in improving classification accuracy of k nearest neighbor, support vector machine and classification and regression tree; (b) STDPNaN also outperforms comparison methods without any restriction on the number of labeled data; (c) the running time of STDPNaN is acceptable.

Unbalanced charge injection is one of the major issues that hampers the efficiency of perovskite light-emitting diodes (PeLEDs). Through engineering the device structure with multiple hole transport layers (HTLs), i.e., poly(9,9-dioctyl-fluorene-co-N-(4-butylphenyl)diphenylamine) (TFB)/poly(9-vinylcarbazole) (PVK) and nickel oxide (NiOx)/TFB/PVK, efficient PeLED devices have been successfully demonstrated. However, in a typical solution-processed PeLED with multiple HTLs, the underlying conjugated HTL could be easily redissolved by the ink of the following one, which not only dramatically deteriorates the electrical property of HTLs but also influences the quality of the top perovskite films. In this work, through inserting a thin atomic layer-deposited aluminum oxide (Al2O3) layer between HTLs and the perovskite layer, an improved interfacial contact can be achieved, which enables us to obtain perovskite films with enhanced characteristics and balanced charge injection in the resultant PeLEDs. In addition, because of the proper refractive index (r), the presence of the Al2O3 layer also favors the light out-coupling of PeLEDs. As a result, we fabricate green PeLEDs with good repeatability and external quantum efficiency of 17.0%, which is approximately 60% higher than that of the control device without Al2O3. Our work provides a promising avenue to enhance interfacial contact between the charge transport layer and perovskite for efficient perovskite-based optoelectronic devices.