Pyrolysis-Gas Chromatography/Mass Spectrometry Analysis of Oils from Different Sources

Regenerated gutter oil (i.e., waste oil) accounts for 10% of the edible oil market, which has caused serious food safety issues. Currently, there is no standard protocol for the identification of the gutter oil. In this study, the pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) method was employed to analyze eleven oil samples including edible vegetable oils (tea oil, corn oil, olive oil, sunflower oil, peanut oil and blend vegetable oil) and waste oils (used frying oil, lard, chicken fat, inferior oil and kitchen waste grease). Three factors of pyrolysis temperature, reaction time and sample volume were investigated to optimize the analytical parameters. The optimal pyrolysis conditions were determined to be 600°C, 1 min and an injection volume of 0.3 μL. Five characteristic components (tetradecane, z,z-9,12-octadecadienoic acid, decanoic acid-2-propenyl ester, 17-octadecenoic acid, and z-9-octadecenoic acid) were found in all oil samples. The existence of C11-C16 olefins in the pyrolytic products of the animal fats and the other low-quality oils could be utilized to distinguish vegetable oils from gutter oils.Â

Risk factors for BK virus infection in DCD donor kidney transplant recipients

BackgroundBK virus infection after kidney transplantation can negatively impact the prognosis of patients. However, current risk factor analyses primarily focus on BK virus nephropathy, while BK viruria and BK viruria progressing to BK viremia receive less attention. This study aims to analyze the risk factors associated with BK viruria and BK viruria progressing to BK viremia in recipients of donation after cardiac death (DCD), with the goal of facilitating early intervention.MethodsDonor characteristics and clinical data of recipients before and after transplantation were evaluated, and logistic univariate and multivariate analyses were performed to determine the risk factors associated with BK viruria and the progression of BK viruria to BK viremia. Additionally, machine learning techniques were employed to identify the top five features associated with BK viruria evolving into BK viremia.ResultsDuring a median follow-up time of 1,072 days (range 739–1,418), 69 transplant recipients (15.6% incidence rate) developed BK viruria after transplantation, with 49.3% of cases occurring within 6 months post-transplantation. Moreover, 19 patients progressed to BK viremia. Donor age [OR: 1.022 (1.000, 1.045), p = 0.047] and donor procalcitonin (PCT) levels [0.5–10 ng/ml; OR: 0.482 (0.280, 0.828), p = 0.008] were identified as independent risk factors for BK viruria. High BK viruria [OR: 11.641 (1.745, 77.678), p = 0.011], recipient age [OR: 1.106 (1.017, 1.202), p = 0.018], and immunoinduction regimen [ATG; OR: 0.063 (0.006, 0.683), p = 0.023] were independent risk factors for BK viruria progressing to BK viremia. Machine learning analysis confirmed the importance of high BK viruria, recipient age, and immunoinduction regimen (ATG) in predicting the progression of BK viruria to BK viremia.ConclusionThe development and progression of BK virus in DCD kidney transplant recipients is influenced by multiple factors. Early intervention and treatment could potentially extend the lifespan of the transplanted organ

Erythropoietin Blockade Inhibits the Induction of Tumor Angiogenesis and Progression

BACKGROUND: The induction of tumor angiogenesis, a pathologic process critical for tumor progression, is mediated by multiple regulatory factors released by tumor and host cells. We investigated the role of the hematopoietic cytokine erythropoietin as an angiogenic factor that modulates tumor progression. METHODOLOGY/PRINCIPAL FINDINGS: Fluorescently-labeled rodent mammary carcinoma cells were injected into dorsal skin-fold window chambers in mice, an angiogenesis model that allows direct, non-invasive, serial visualization and real-time assessment of tumor cells and neovascularization simultaneously using intravital microscopy and computerized image analysis during the initial stages of tumorigenesis. Erythropoietin or its antagonist proteins were co-injected with tumor cells into window chambers. In vivo growth of cells engineered to stably express a constitutively active erythropoietin receptor EPOR-R129C or the erythropoietin antagonist R103A-EPO were analyzed in window chambers and in the mammary fat pads of athymic nude mice. Co-injection of erythropoietin with tumor cells or expression of EPOR-R129C in tumor cells significantly stimulated tumor neovascularization and growth in window chambers. Co-injection of erythropoietin antagonist proteins (soluble EPOR or anti-EPO antibody) with tumor cells or stable expression of antagonist R103A-EPO protein secreted from tumor cells inhibited angiogenesis and impaired tumor growth. In orthotopic tumor xenograft studies, EPOR-R129C expression significantly promoted tumor growth associated with increased expression of Ki67 proliferation antigen, enhanced microvessel density, decreased tumor hypoxia, and increased phosphorylation of extracellular-regulated kinases ERK1/2. R103A-EPO antagonist expression in mammary carcinoma cells was associated with near-complete disruption of primary tumor formation in the mammary fat pad. CONCLUSIONS/SIGNIFICANCE: These data indicate that erythropoietin is an important angiogenic factor that regulates the induction of tumor cell-induced neovascularization and growth during the initial stages of tumorigenesis. The suppression of tumor angiogenesis and progression by erythropoietin blockade suggests that erythropoietin may constitute a potential target for the therapeutic modulation of angiogenesis in cancer

Color Occlusion Face Recognition Method Based on Quaternion Non-Convex Sparse Constraint Mechanism

As the acquisition and application of color images become more and more extensive, color face recognition technology has also been vigorously developed, especially the recognition methods based on convolutional neural network, which have excellent performance. However, with the increasing depth and complexity of network models, the number of calculated parameters also increases, which means the training of most high-performance models depends on large-scale samples and expensive equipment. Therefore, the key to the current research is to realize a lightweight model while ensuring the recognition accuracy. At present, PCANet, a typical lightweight framework for deep learning, has achieved good results in most of the image recognition tasks, but its recognition accuracy for color face images, especially under occlusion, still needs to be improved. Therefore, a color occlusion face recognition method based on quaternion non-convex sparse constraint mechanism is proposed in this paper. Firstly, a quaternion non-convex sparse principal component analysis network model was constructed based on Lp regularization of strong sparsity. Secondly, the fixed point iteration method and coordinate descent method were established to solve the non-convex optimization problem. Finally, the occlusion recognition performance of the proposed method was verified on Georgia Tech, Color FERET, AR, and LFW-A Color face datasets

A Video Sequence Face Expression Recognition Method Based on Squeeze-and-Excitation and 3DPCA Network

Expression recognition is a very important direction for computers to understand human emotions and human-computer interaction. However, for 3D data such as video sequences, the complex structure of traditional convolutional neural networks, which stretch the input 3D data into vectors, not only leads to a dimensional explosion, but also fails to retain structural information in 3D space, simultaneously leading to an increase in computational cost and a lower accuracy rate of expression recognition. This paper proposes a video sequence face expression recognition method based on Squeeze-and-Excitation and 3DPCA Network (SE-3DPCANet). The introduction of a 3DPCA algorithm in the convolution layer directly constructs tensor convolution kernels to extract the dynamic expression features of video sequences from the spatial and temporal dimensions, without weighting the convolution kernels of adjacent frames by shared weights. Squeeze-and-Excitation Network is introduced in the feature encoding layer, to automatically learn the weights of local channel features in the tensor features, thus increasing the representation capability of the model and further improving recognition accuracy. The proposed method is validated on three video face expression datasets. Comparisons were made with other common expression recognition methods, achieving higher recognition rates while significantly reducing the time required for training

Three State Estimation Fusion Methods Based on the Characteristic Function Filtering

There are three state estimation fusion methods for a class of strong nonlinear measurement systems, based on the characteristic function filter, namely the centralized filter, parallel filter, and sequential filter. Under ideal communication conditions, the centralized filter can obtain the best state estimation accuracy, and the parallel filter can simplify centralized calculation complexity and improve feasibility; in addition, the performance of the sequential filter is very close to that of the centralized filter and far better than that of the parallel filter. However, the sequential filter can tolerate non-ideal conditions, such as delay and packet loss, and the first two filters cannot operate normally online for delay and will be invalid for packet loss. The performance of the three designed fusion filters is illustrated by three typical cases, which are all better than that of the most popular Extended Kalman Filter (EKF) performance

Integrated and Control-Oriented Simulation Tool for Optimizing Urban Drainage System Operation

With the management and operation of urban drainage systems (UDS) becoming more complicated and difficult, integrated models aiming to control and manage the entire drainage system are under enormous demand. Ideally, integrated models, as a potential tool for meeting the increasing demands, should combine both conceptual and mechanistic models that merge all UDS components and balance simulation accuracy with time constraints. Within this context, our study introduces an innovative modeling software, Simuwater, which couples multiple principles, simulates multiple components, and combines optimized control functions, playing a role in the integrated simulation and overflow control application of UDS. The software has been utilized in a real-time case-control study in one city of China, and it obtained significant optimized operation results to reduce combined sewer overflow (CSO) by making full use of the storage facilities and actuators. As the Simuwater model continues to improve in depth and breadth, it will play an increasingly important role in more application scenarios of UDS

Functional characterization of C-TERMINALLY ENCODED PEPTIDE (CEP) family in Brassica rapa L

The small regulatory C-TERMINALLY ENCODED PEPTIDE (CEP) peptide family plays crucial roles in plant growth and stress response. However, little is known about this peptide family in Brassica species. Here, we performed a systematic analysis to identify the putative Brassica rapa L. CEP (BrCEP) gene family. In total, 27 BrCEP genes were identified and they were classified into four subgroups based on the CEP motifs similarity. BrCEP genes displayed distinct expression patterns in response to both developmental and several environmental signals, suggesting their broad roles during Brassica rapa development. Furthuremore, the synthetic BrCEP3 peptide accelerated Brassica rapa primary root growth in a hydrogen peroxide (H2O2) and Ca2+ dependent manner. In summary, our work will provide fundamental insights into the physiological function of CEP peptides during Brassica rapa development