Up-regulation of epithelial growth factor receptor (EGFR) and down-regulation of E-cadherin are correlated with genesis, progression, invasion, and metastasis of tumor; however,their expressions,especially their association,in esophageal carcinoma have seldom been reported. This study was to detect expressions of EGFR and E-cadherin in esophageal carcinoma, and to analyze their relationship.Expressions of EGFR and E-cadherin in 50 specimens of esophageal squamous carcinoma and 8 specimens of esophageal adenocarcinoma were detected by immunohistochemistry.Positive rates of EGFR, and E-cadherin in esophageal squamous carcinoma were 72.0% (36/50), and 22.0% (11/50); while those in esophageal adenocarcinoma were 75.0% (6/8), and 25% (2/8). Positive rates of EGFR in esophageal squamous carcinoma of grades I, II, and III were 63.6%, 75.0%, and 81.3%, respectively. The expression of EGFR negatively correlated with that of E-cadherin (r=-0.341, P=0.008).The expression of EGFR in esophageal carcinoma is increased, while that of E-cadherin is decreased;down-regulation of E-cadherin may be associated with up-regulation of EGFR.
CD44 describes a family of surface proteins consisting of many isoforms due to alternative splice of ten variant exons. Members of this family are involved in various processes including hematopoiesis, lymphocyte activation and homing, limb development, wound healing and tumor progression. Clinically, CD44 has been shown to be a prognostic factor for several human cancers. To answer the question which isoform might be relevant for tumor progression and to gain an insight into the mechanism of its function, I established transfectants of the LB lymphoma cell line in which the expression of four CD44 isoforms, namely CD44v3-10, CD44v4-10, CD44v8-10 and CD44s, was controled by the Tet-off promoter. In the prescence of Doxycycline, the expression was repressed. Removal of Doxycycline switched on expression and the maximal CD44 amount was obtained within two days. The transfectants were characterized regarding their ability to bind to the extracellular matrix component hyaluronate (HA). Overexpression of all four CD44 isoforms conferred the ability to bind HA on LB cells. Other glycosaminoglycans (GAGs) were bound in an isotype-specific fashion. CD44v3-10, CD44v4-10 and CD44v8-10 showed high binding affinity to chondroitin A, B and C, and low affinity to heparin, heparan sulfate and keratan sulfate. CD44s could not bind to these GAGs. Among these three variants, the binding ability of CD44v3-10 was the strongest. CD44 clustering seemed to play a crucial role for HA binding. Both CD44s and CD44v8-10 formed reduction-sensitive complexes in LB cells. The complexes are homooligomers or heterooligomers composed of different isoforms. Cys286 in CD44 transmember domain was not responsible for the formation of reduction-sensitive oligomer or for the enhanced HA binding in LB cell line. Using a conditional dimerization system the requirement of CD44 oligomerization for HA binding was directly demonstrated. The induction of oligomerization increased HA binding. Finally, I studied the role of CD44 in lymphoma development by subcutaneous injection of the transfectants in syngeneic or immunocompromised mice. I found no influence of CD44v3-10, CD44v4-10 and CD44v8-10 isoforms on the LB lymphoma formation and metastasis.
The authors declare no conflict of interest. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Objective This study was conducted to examine the causal association between exercise and the risk of dementia among older Chinese adults. Design Longitudinal population-based study with a follow-up duration of 9 years. Setting Data for the Chinese Longitudinal Healthy Longevity Survey waves occurring from 2002 to 2011–2012 were extracted from the survey database. Participants In total, 7501 dementia-free subjects who were older than 65 years were included at baseline. Dementia was defined as a self-reported or proxy-reported physician’s diagnosis of the disease. Outcome measures and methods Regular exercise and potential confounding variables were obtained via a self-report questionnaire. We generated longitudinal logistic regression models based on time-lagged generalised estimating equation to examine the causal association between exercise and dementia risk. Results Of the 7501 older Chinese people included in this study, 338 developed dementia during the 9-year follow-up period after excluding those who were lost to follow-up or deceased. People who regularly exercised had lower odds of developing dementia (OR=0.53, 95% CI 0.33 to 0.85) than those who did not exercise regularly. Conclusion Regular exercise was associated with decreased risk of dementia. Policy-makers should develop effective public health programmes and build exercise-friendly environments for the general public.
As an advanced non-destructive testing and quality control technique, industrial computed tomography (ICT) has found many applications in smart manufacturing. The existing ICT devices are usually bulky and involve mass data processing and transmission. It results in a low efficiency and cannot keep pace with smart manufacturing. In this paper, with the support from Internet of things (IoT) and convolutional neural network (CNN), we proposed a lightweight solution of ICT devices for smart manufacturing. It consists of efforts from two aspects: distributed hardware allocation and data reduction. At the first aspect, ICT devices are separated into four functional units: data acquisition, cloud storage, computing center and control terminals. They are distributed and interconnected by IoT. Only the data acquisition unit still remains in the production lines. This distribution not only slims the ICT device, but also permits the share of the same functional units. At the second aspect, in the data acquisition unit, sparse sampling strategy is adopted to reduce the raw data and singular value decomposition (SVD) is used to compress these data. They are then transmitted to the cloud storage. At the computing center, an ICT image reconstruction algorithm and a CNN are applied to these compressed sparse sampling data to obtain high quality CT images. The experiments with practical ICT data have been executed to demonstrate the validity of the proposed solution. The results indicate that this solution can achieve a drastic data reduction, a storage space save and an efficiency improvement without significant image degradation. The presented work has been helpful to push the applications of ICT in smart manufacturing.
Aerial seeding based on the unmanned agricultural aerial system (UAAS) improves the seeding efficiency of oilseed rape (OSR) seeds, and solves the problem of OSR planting in mountainous areas where it is inconvenient to use ground seeding machines. Therefore, the UAAS has been applied in aerial seeding to a certain degree in China. The effective broadcast seeding width (EBSW), broadcast seeding density (BSD) and broadcast seeding uniformity (BSU) are the important indexes that affect the aerial seeding efficiency and quality of OSR seeds. In order to investigate the effects of flight speed (FS) and flight height (FH) on EBSW, BSD and BSU, and to achieve the optimized parameter combinations of UAAS T30 on aerial seeding application, three levels of FS (4.0 m/s, 5.0 m/s and 6.0 m/s) and three levels of FH (2.0 m, 3.0 m and 4.0 m) experiments were carried out in the field with 6.0 kg seeds per ha. The results demonstrated that the EBSW was not constant as the FS and FH changed. In general, the EBSW showed a change trend of first increasing and then decreasing as the FH increased under the same FS, and showed a trend of decreasing as FS increased under the same FH. The EBSWs were over 3.0 m in the nine treatments, in which the maximum was 5.44 m (T1, 4.0 m/s, 2.0 m) while the minimum was 3.2 m (T9, 6.0 m/s, 4.0 m). The BSD showed a negative change correlation as the FS changed under the same FH, and the BSD decreased as the FH increased under 4.0 m/s FS, while it first increased and then decreased under the FS of 5.0 m/s and 6.0 m/s. The maximum BSD value was 140.12 seeds/m2 (T1, 4.0 m/s, 2.0 m), while the minimum was 40.17 seeds/m2 (T9, 6.0 m/s, 4.0 m). There was no obvious change in the trend of the BSU evaluated by the coefficients of variation (CV): the minimum CV was 13.01% (T6, 6.0 m/s, 3.0 m) and the maximum was 64.48% (T3, 6.0 m/s, 2.0 m). The statistical analyses showed that the FH had significant impacts on the EBSWs (0.01 < p-value < 0.05), the FS and the interaction between FH and FS both had extremely significant impacts on EBSWs (p-value < 0.01). The FH had extremely significant impacts on BSD (p-value < 0.01), the FS had no impacts on BSD (p-value > 0.05), and the interaction between FH and FS had significant impacts on BSD (0.01 < p-value < 0.05). There were no significant differences in the broadcast sowing uniformity (BSU) among the treatments. Taking the EBSW, BSD and BSU into consideration, the parameter combination of T5 (T9, 5.0 m/s, 3.0 m) was selected for aerial seeding. The OSR seed germination rate was over 36 plants/m2 (33 days) on average, which satisfied the requirements of OSR planting agronomy. This study provided some technical support for UAAS application in aerial seeding.
Differential phase-contrast computed tomography (DPC-CT) is a powerful analysis tool for soft-tissue and low-atomic-number samples. Limited by the implementation conditions, DPC-CT with incomplete projections happens quite often. Conventional reconstruction algorithms face difficulty when given incomplete data. They usually involve complicated parameter selection operations, which are also sensitive to noise and are time-consuming. In this paper, we report a new deep learning reconstruction framework for incomplete data DPC-CT. It involves the tight coupling of the deep learning neural network and DPC-CT reconstruction algorithm in the domain of DPC projection sinograms. The estimated result is not an artifact caused by the incomplete data, but a complete phase-contrast projection sinogram. After training, this framework is determined and can be used to reconstruct the final DPC-CT images for a given incomplete projection sinogram. Taking the sparse-view, limited-view and missing-view DPC-CT as examples, this framework is validated and demonstrated with synthetic and experimental data sets. Compared with other methods, our framework can achieve the best imaging quality at a faster speed and with fewer parameters. This work supports the application of the state-of-the-art deep learning theory in the field of DPC-CT.
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