Risk factors for high-stage histological chorioamnionitis among pregnancies with cervical incompetence.

AIM: The study aimed to identify predictive risk factor to identify high-stage histological chorioamnionitis (HCA) in pregnancies with cervical incompetence (CIC). METHODS: A retrospective cohort study was conducted by including 116 pregnant women with cervical incompetence that required prophylactical and therapeutical cerclage. The histopathology examination on placenta was conducted with informed patient consent. All the cases included in this study were divided based on the severity degree of HCA. The demographic characteristic and the parameters related to maternal and fetal outcome were all analyzed. Besides, perioperative parameters of cerclage, including cervical length, cervical morphology, and laboratory indexes were also compared between two groups. Univariate and multivariate logistic regression analysis were used to determine the risk factor of severe chorioamnionitis. RESULTS: Severe HCA was significantly associated with cervical morphology, cerclage indication, cerclage type, and cervical length measured via ultrasound and vaginal examination. After adjusted for confounders, V-type funneling and short cervix was indicated as independent risk factors of severe HCA by multivariate logistic regression analysis, respectively. CONCLUSIONS: V-type funneling and short cervix may indicate the elevated risk of high-stage HCA. Due to the negative outcomes related with high-stage HCA, appropriate prenatal treatment would improve the pregnancy outcomes in cerclaged population. To facilitate postpartum treatment, placental histological examination should be routinely recommended to identify the high-stage HCA, especially in high risk pregnancies.

FIBP is a prognostic biomarker and correlated with clinicalpathological characteristics and immune infiltrates in acute myeloid leukemia.

Acute myeloid leukemia (AML) is one of the most common hematological malignancy that has a high recurrence rate. FIBP was reported to be highly expressed in multiple tumor types. However, its expression and role in acute myeloid leukemia remains largely unknown. The aim of this study was to clarify the role and value of FIBP in the diagnosis and prognosis, and to analyze its correlation with immune infiltration in acute myeloid leukemia by The Cancer Genome Atlas (TCGA) dataset. FIBP was highly expressed in AML samples compared to normal samples. The differentially expressed genes were identified between high and low expression of FIBP. The high FIBP expression group had poorer overall survival. FIBP was closely correlated with CD4, IL-10 and IL-2. The enrichment analysis indicated DEGs were mainly related to leukocyte migration, leukocyte cell-cell adhesion, myeloid leukocyte differentiation, endothelial cell proliferation and T cell tolerance induction. FIBP expression has significant correlation with infiltrating levels of various immune cells. FIBP could be a potential targeted therapy and prognostic biomarker associated with immune infiltrates for AML.

Driver identification and fatigue detection algorithm based on deep learning.

In order to avoid traffic accidents caused by driver fatigue, smoking and talking on the phone, it is necessary to design an effective fatigue detection algorithm. Firstly, this paper studies the detection algorithms of driver fatigue at home and abroad, and analyzes the advantages and disadvantages of the existing algorithms. Secondly, a face recognition module is introduced to crop and align the acquired faces and input them into the Facenet network model for feature extraction, thus completing the identification of drivers. Thirdly, a new driver fatigue detection algorithm based on deep learning is designed based on Single Shot MultiBox Detector (SSD) algorithm, and the additional layer network structure of SSD is redesigned by using the idea of reverse residual. By adding the detection of drivers' smoking and making phone calls, adjusting the size and number of prior boxes of SSD algorithm, improving FPN network and SE network, the identification and verification of drivers can be realized. The experimental results showed that the number of parameters decreased from 96.62 MB to 18.24 MB. The average accuracy rate increased from 89.88% to 95.69%. The projected number of frames per second increased from 51.69 to 71.86. When the confidence threshold was set to 0.5, the recall rate of closed eyes increased from 46.69% to 65.87%, that of yawning increased from 59.72% to 82.72%, and that of smoking increased from 65.87% to 83.09%. These results show that the improved network model has better feature extraction ability for small targets.

Research on application of helmet wearing detection improved by YOLOv4 algorithm.

Aiming at the problem that the model of YOLOv4 algorithm has too many parameters and the detection effect of small targets is poor, this paper proposes an improved helmet fitting detection model based on YOLOv4 algorithm. Firstly, this model improves the detection accuracy of small targets by adding multi-scale prediction and improving the structure of PANet network. Then, the improved depth-separable convolution was used to replace the standard 3 × 3 convolution, which greatly reduced the model parameters without reducing the detection ability of the model. Finally, the k_means clustering algorithm is used to optimize the prior box. The model was tested on the self-made helmet dataset helmet_dataset. Experimental results show that compared with the safety helmet detection model based on Faster RCNN algorithm, the improved YOLOv4 algorithm has faster detection speed, higher detection accuracy and smaller number of model parameters. Compared with the original YOLOv4 model, the mAP of the improved YOLOv4 algorithm is increased by 0.49%, reaching 93.05%. The number of model parameters was reduced by about 58%, to about 105 MB. The model reasoning speed is 35 FPS. The improved YOLOv4 algorithm can meet the requirements of helmet wearing detection in multiple scenarios.

Research on Simultaneous localization and mapping Algorithm based on Lidar and IMU.

In recent years, the research of autonomous driving and mobile robot technology is a hot research direction. The ability of simultaneous positioning and mapping is an important prerequisite for unmanned systems. Lidar is widely used as the main sensor in SLAM (Simultaneous Localization and Mapping) technology because of its high precision and all-weather operation. The combination of Lidar and IMU (Inertial Measurement Unit) is an effective method to improve overall accuracy. In this paper, multi-line Lidar is used as the main data acquisition sensor, and the data provided by IMU is integrated to study robot positioning and environment modeling. On the one hand, this paper proposes an optimization method of tight coupling of lidar and IMU using factor mapping to optimize the mapping effect. Use the sliding window to limit the number of frames optimized in the factor graph. The edge method is used to ensure that the optimization accuracy is not reduced. The results show that the point plane matching mapping method based on factor graph optimization has a better mapping effect and smaller error. After using sliding window optimization, the speed is improved, which is an important basis for the realization of unmanned systems. On the other hand, on the basis of improving the method of optimizing the mapping using factor mapping, the scanning context loopback detection method is integrated to improve the mapping accuracy. Experiments show that the mapping accuracy is improved and the matching speed between two frames is reduced under loopback mapping. However, it does not affect real-time positioning and mapping, and can meet the requirements of real-time positioning and mapping in practical applications.

The Dice measure of cubic hesitant fuzzy sets and its initial evaluation method of benign prostatic hyperplasia symptoms.

In order to give the initial evaluation of benign prostatic hyperplasia (BPH) symptoms regarding a patient, physician usually performs the clinical inquiry of the patient, whereas his/her responses may contain hesitant fuzzy and uncertain information. However, existing evaluation/diagnosis approaches of BPH symptoms cannot cope with the hybrid problem of both hesitant and uncertain responses of patients. Furthermore, existing evaluation approaches may lose some useful responses (e.g. hesitant fuzzy information) so as to result in the unreasonable or indeterminate evaluation/diagnosis in the evaluation process of patients. To overcome this insufficiency, this study firstly introduces the concept of a cubic hesitant fuzzy set (CHFS) based on combining uncertain/interval-valued fuzzy information with hesitant fuzzy information so as to express the hybrid fuzzy information and proposes the Dice measure between CHFSs based on the extension method of the least common multiple cardinality/number (LCMC) for the hesitant fuzzy sets in CHFS. Then, the initial evaluation approach of BPH symptoms is developed by using the Dice measure of CHFSs. Lastly, the assessment results of six BPH patients are presented as the clinical actual cases to indicate the applicability and effectiveness of the developed assessment approach in CHFS setting. The comparison with existing common evaluation methods shows that the developed evaluation/diagnosis method is superior to the existing common evaluation methods in the evaluation/diagnosis process of the clinical actual cases.

An evaluation method of risk grades for prostate cancer using similarity measure of cubic hesitant fuzzy sets.

Prostate cancer (PC) is more common cancer in older men. Then, the existing evaluation method of PC risk grades is based on the AJCC (American Joint Committee on Cancer) staging/scoring system. It utilizes the comprehensive risk data of the prostate-specific antigen (PSA) test, Gleason score, and T2 staging score as the evaluation criteria of PC patients. However, these risk data of PC patients not only may belong to different risk grades simultaneously to result in the unreasonable and uncertain evaluation results to some extent, but also may lose useful fuzzy and uncertain information in the existing evaluation method with non-fuzzy information. To overcome these insufficiencies, the research problems in this study are: (a) to present a new concept of a cubic hesitant fuzzy set (CHFS) for expressing uncertain and hesitant fuzzy information; (b) to propose the generalized distance and similarity measure between CHFSs; (c) to establish a comprehensive evaluation method of PC risk grades with CHFS information by using the similarity measure of CHFSs; and (d) to provide the evaluation examples of PC patients as actual clinical cases for indicating the rationality and effectiveness of the proposed risk evaluation method. Then, the main contribution of this original study is that we present a new concept of CHFS to express uncertain and hesitant information of PC risk grades and the generalized distance-based similarity measure of CHFSs to establish a comprehensive evaluation method of PC risk grades. Finally, by the 16 evaluation examples of the PC patients, all their evaluation results verify the rationality and effectiveness of the proposed comprehensive evaluation method. The comparative analysis demonstrates that its evaluation performance is superior to that of the existing evaluation method of PC risk grades.

Exponential Entropy for Simplified Neutrosophic Sets and Its Application in Decision Making.

Entropy is one of many important mathematical tools for measuring uncertain/fuzzy information. As a subclass of neutrosophic sets (NSs), simplified NSs (including single-valued and interval-valued NSs) can describe incomplete, indeterminate, and inconsistent information. Based on the concept of fuzzy exponential entropy for fuzzy sets, this work proposes exponential entropy measures of simplified NSs (named simplified neutrosophic exponential entropy (SNEE) measures), including single-valued and interval-valued neutrosophic exponential entropy measures, and investigates their properties. Then, the proposed exponential entropy measures of simplified NSs are compared with existing related entropy measures of interval-valued NSs to illustrate the rationality and effectiveness of the proposed SNEE measures through a numerical example. Finally, the developed exponential entropy measures for simplified NSs are applied to a multi-attribute decision-making example in an interval-valued NS setting to demonstrate the application of the proposed SNEE measures. However, the SNEE measures not only enrich the theory of simplified neutrosophic entropy, but also provide a novel way of measuring uncertain information in a simplified NS setting.

Generalized Distance-Based Entropy and Dimension Root Entropy for Simplified Neutrosophic Sets.

In order to quantify the fuzziness in the simplified neutrosophic setting, this paper proposes a generalized distance-based entropy measure and a dimension root entropy measure of simplified neutrosophic sets (NSs) (containing interval-valued and single-valued NSs) and verifies their properties. Then, comparison with the existing relative interval-valued NS entropy measures through a numerical example is carried out to demonstrate the feasibility and rationality of the presented generalized distance-based entropy and dimension root entropy measures of simplified NSs. Lastly, a decision-making example is presented to illustrate their applicability, and then the decision results indicate that the presented entropy measures are effective and reasonable. Hence, this study enriches the simplified neutrosophic entropy theory and measure approaches.

A Novel Method of Multi-Information Acquisition for Electromagnetic Flow Meters.

In this paper, a novel method is proposed for multi-information acquisition from the electromagnetic flow meter, using magnetic excitation to measure the fluid velocity and electrochemistry impedance spectroscopy (EIS) for both the fluid quality and the contamination level of the transducer. The impedance spectra of the transducer are measured with an additional electrical stimulus in series with the electrode measurement loop. The series connection mode instead of the parallel one improves the signal-to-noise ratio (SNR) of the fluid velocity measurement and offers a wide range of impedance measurements by using a sample capacitance. In addition, a multi-frequency synchronous excitation source is synthesized based on the method of dual-base power sequences for fast EIS measurement. The conductivity measurements in the range of 1.7 µS/cm-2 mS/cm showed a relatively high accuracy with a measurement error of 5%, and the electrode adhesion detection on both with coating and no coating showed the ability of the qualitative determination of the electrode adhesion, which validated the feasibility of the multi-information acquisition method for the electromagnetic flow meter (EMFM).

Biotransformation of phenolic tetrahydroprotoberberines in plant cell cultures followed by LC-NMR, LC-MS, and LC-CD.

A metabolic pathway of 2,3,10,11-oxygenated tetrahydroprotoberberines having the OH group on ring D was demonstrated. Metabolism of (13)C- or D(2)-labeled precursors was studied in cell cultures of Macleaya, Corydalis, and Nandina species. The structures of alkaloid metabolites obtained from feeding experiments were determined by application of combined LC-NMR, LC-MS/MS, and LC-CD techniques. (S)-Tetrahydropseudoprotoberberine (5) was stereospecifically O-methylated to the S-isomer (12) in cell cultures of three plant species. This S-isomer was further N-methylated to the (S)-alpha-N-methyl salt (15), which was oxidized to produce the pseudoprotopine-type alkaloid (10) in cell cultures of Macleaya and Corydalis species. These transformations were the same as those of 2,3,9,10-oxygenated protoberberines. The tetrahydropseudoprotoberberines (5, 6, and 12) were dehydrogenated to pseudoprotoberberines (13, 16, and 14), respectively. Both the R- and S-enantiomers of 5 were dehydrogenated in Macleaya cordata different from the case of 2,3,9,10-oxygenated protoberberines. Precursor 7, with OH groups at C-10 and C-11, was O-methylated at C-10 in M. cordata and C. ochotensis var. raddeana, which was distinct from O-methylation in N. domestica, in which 7 was O-methylated at both C-11 and C-10. Stereoselective O-demethylation [(S)-5 to (S)-18] occurred in N. domestica.

Enantiomeric separation of racemic 1-benzyl-N-methyltetrahydroisoquinolines on chiral columns and chiral purity determinations of the O-methylated metabolites in plant cell cultures by HPLC-CD on-line coupling in combination with HPLC-MS.

Effective enantiomeric separations of 1-benzyl-N-methyltetrahydroisoquinolines were achieved using commercially available Chiralcel OD-H and OJ-H columns. Online LC-CD analysis allowed for the establishment of a correlation between the absolute configuration of the separated enantiomers and their characteristic CD transitions. LC-MS combined with LC-CD analysis permitted chiral purity determinations of O-methylated metabolites of nine phenolic 1-benzyl-N-methyltetrahydroisoquinolines in cell cultures of Corydalis, Macleaya, and Nandina species.

Biotransformation of phenolic 1-benzyl-N-methyltetrahydroisoquinolines in plant cell cultures followed by LC/NMR, LC/MS, and LC/CD.

(+/-)-1-Benzyl- N-methyltetrahydroisoquinolines 7-10 and 11-14 with one and two hydroxy groups on the aromatic rings, respectively, were fed individually to cultured cells of Corydalis and Macleaya species, respectively. The structures of the metabolites were determined by using combinatorial techniques, including LC/NMR, LC/MS-MS, and LC/CD. The enantiomeric excesses of the metabolites were derived from LC/CD and LC/MS-MS analyses. In cell cultures of Corydalis and Macleaya species, laudanine (7), with a hydroxy group at C-3', can form the berberine bridge at C-2' and C-6' to produce S- and R-enantiomers of 2,3,9,10- and 2,3,10,11-oxygenated protoberberines (20 and 21), respectively, whereas reticuline (11) and protosinomenine (12), incoporating a hydroxy group at C-3', form the berberine bridge at C-2' to furnish the S-enantiomer of 2,3,9,10-oxygenated protoberberines (23 and 21), respectively.

Potential cancer chemopreventive activity of simple isoquinolines, 1-benzylisoquinolines, and protoberberines.

Seventeen simple isoquinolines, 15 1-benzylisoquinolines, and 19 protoberberines were tested for their inhibitory activities against Epstein-Barr virus early antigen (EBV-EA) activation induced by 12-O-tetradecanoylphorbol-13-acetate (TPA) in Raji cells. Among the tested alkaloids, the inhibitory activity of all 1-benzylisoquinolines and 11 protoberberines was higher than that of beta-carotene. The 1-benzylisoquinolines 19, 21, 22, 29, and 34 and protoberberines 41, 47-49, 51, 52, and 55 showed potent inhibitory effects on EBV-EA induction (96-100% inhibition at 1 x 10(3) mol ratio/TPA). These alkaloids were more active than the naturally occurring alkaloids, 23, 25, 33, 53, and 54. In addition, fifteen simple isoquinolines, eighteen 1-benzylisoquinolines and eight protoberberines were evaluated with respect to their ability to scavenge 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radicals. Nine simple isoquinolines, ten 1-benzylisoquinolines, and four protoberberines were more potent than alpha-tocopherol, and four 1-benzylisoquinolines, 20 and 28-30, exhibited potent activities (SC50 4.5-5.8 microM). Their activities were higher than the naturally occurring alkaloids, 23, 25, and 33. Therefore, some of the isoquinoline alkaloids indicating the high activity on both assays may be potentially valuable cancer chemopreventive agents. Structure-activity relationships are discussed for both tests.

Structural analyses of metabolites of phenolic 1-benzyltetrahydroisoquinolines in plant cell cultures by LC/NMR, LC/MS, and LC/CD.

The metabolism of the phenolic 1-benzyltetrahydroisoquinoline alkaloids was studied in cell cultures of Macleaya and Corydalis species. The crude alkaloid fraction obtained from feeding experiments was investigated by application of the combined LC/NMR and LC/APCI-MS (/MS) techniques. Several metabolites were detected, and their structures (6 and 8-14) were identified. Bioconversion of the phenolic 1-benzyltetrahydroisoquinoline (2) into the pseudoprotoberberine (8) was demonstrated for the first time. LC/APCI-MS and LC/CD experiments carried out on a chiral column permitted the deduction of the major enantiomeric form of the chiral metabolites. Thus, the combination of NMR, MS, and CD data permitted the structural elucidation and stereochemical analysis of the metabolites in the extract matrix solution, without isolation and sample purification prior to the coupling experiments.