Modeling and anti-sway control method for vertical lift-up/lay-down process of slender-beam payload.

The existing crane control methods mainly aim at suppressing the oscillation of the payloads during the transportation process. However, during the vertical lift-up/lay-down process of the slender-beam payload (SBP), such as the installation of the wind turbine towers, rocket assembly, and pipeline laying, one end of the SBP is lifted up and the other end is in contact with the ground. In this situation, the external disturbance may cause the SBP to sway around the contact point. In this paper, a nonlinear three-dimensional dynamic model for the vertical lift-up/lay-down process of the SBP is established. On this basis, a control method for suppressing oscillation during the lift-up/lay-down process of the SBP is proposed by adopting the non-singular terminal sliding mode control (NTSMC). The stability of the NTSMC is proved based on the Lyapunov method. Simulations and experiments are carried out to verify the effectiveness of the proposed method.

Mortality burden and future projections of major risk factors for esophageal cancer in China from 1990 to 2019.

OBJECTIVE: This study, based on Global Burden of Disease (GBD) data, aimed to report the long-term trend in mortality rates caused by risk factors for esophageal cancer (EC) in China from 1990 to 2019 and predict the burden of EC mortality caused by these risk factors over the next 15 years. METHODS: We examined six risk factors that influenced EC mortality rates in China and their respective rankings. Furthermore, we analyzed the number of deaths and crude mortality rates (CMR) caused by these risk factors for both sexes and different age groups. Age-standardized mortality rates (ASMR) and the number of deaths across all age groups were also analyzed. Finally, we utilized the Bayesian Age-Period-Cohort (BAPC) model to predict the trends in ASMR burden caused by these risk factors in the future. RESULTS: From 1990 to 2019, the percentage changes in ASMR for EC caused by the six risk factors in China were as follows: smoking (- 33.4%), alcohol consumption (- 23.0%), low fruit intake (- 73.6%), low vegetable intake (- 96.0%), high Body Mass Index (BMI) (25.1%), and tobacco chewing (- 32.8%). In 2019, the top three risk factors contributing to EC ASMR in China were smoking, alcohol consumption, and high BMI. Overall, the ASMR for EC in China fluctuated and declined from 1990 to 2019. The most common risk factors for males were smoking and alcohol consumption, while low fruit intake and high BMI were the most common risk factors for females. The impact of these risk factors on EC mortality increased with age, except for the elderly population. BAPC analysis indicated that the influence of these risk factors on ASMR was expected to remain relatively stable in the next 15 years, suggesting a continued significant burden of EC. CONCLUSION: The projected burden of EC mortality in China was expected to continue increasing steadily over the next 15 years, highlighting the pressing need for disease control measures. To alleviate this burden, targeted prevention and control policies addressing risk factors for EC such as smoking, alcohol consumption, and high BMI are necessary.

A Spatial-Temporal Graph Model for Pronunciation Feature Prediction of Chinese Poetry.

With the development of artificial intelligence, speech recognition and prediction have become one of the important research domains with wild applications, such as intelligent control, education, individual identification, and emotion analysis. Chinese poetry reading contains rich features of continuous pronunciations, such as mood, emotion, rhythm schemes, lyric reading, and artistic expression. Therefore, the prediction of the pronunciation characteristics of a Chinese poetry reading is the significance for the presentation of high-level machine intelligence and has the potential to create a high-level intelligent system for teaching children to read Tang poetry. Mel frequency cepstral coefficient (MFCC) is currently used to present important speech features. Due to the complexity and high degree of nonlinearity in poetry reading, however, there is a tough challenge facing accurate pronunciation feature prediction, that is, how to model complex spatial correlations and time dynamics, such as rhyme schemes. As for many current methods, they ignore the spatial and temporal characteristics in MFCC presentation. In addition, these methods are subjected to certain limitations on prediction for long-term performance. In order to solve these problems, we propose a novel spatial-temporal graph model (STGM-MHA) based on multihead attention for the purpose of pronunciation feature prediction of Chinese poetry. The STGM-MHA is designed using an encoder-decoder structure. The encoder compresses the data into a hidden space representation, while the decoder reconstructs the hidden space representation as output. In the model, a novel gated recurrent unit (GRU) module (AGRU) based on multihead attention is proposed to extract the spatial and temporal features of MFCC data effectively. The evaluation comparison of our proposed model versus state-of-the-art methods in six datasets reveals the clear advantage of the proposed model.

Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity.

Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.

Clinical significance of the expression of DNA methyltransferase proteins in gastric cancer.

DNA methyltransferase (DNMT) 1, DNMT3A and DNMT3B, which affect promoter CpG methylation status, play a significant role in cancer development. Little is known regarding the clinical significance of DNMT expression in gastric cancers. Expression of DNMT1, DNMT3A and DNMT3B in paraffin sections from 54 gastric cancer patients were examined using immunohistochemistry, and their associations with the corresponding clinicopathological parameters were analyzed using the Chi-square test. Overexpression of DNMT1, DNMT3A and DNMT3B in gastric cancer tissues was observed in 35 (64.8%), 38 (70.4%) and 28 (51.9%) of 54 cases, respectively. DNMT1 was localized in the cytoplasm and nuclei of the cancer cells, whereas DNMT3A and DNMT3B were detected only in the cytoplasm. DNMT1 expression was more frequently found in tumors localizing at the cardia or body of the stomach (P=0.048). DNMT3A was associated with TNM stage (P=0.001) and lymph node metastasis (P=0.002). No significant correlation was found between DNMT3B expression and clinicopathological data (P>0.05). The co-expression of DNMT1 and DNMT3A, and of DNMT3A and DNMT3B was more frequently found in tumors localizing at the cardia or body of the stomach (P=0.005 and P=0.009 respectively). Moreover, co-expression of DNMT1 and DNMT3A was significantly associated with lymph node metastasis (P=0.035). DNMTs are overexpressed in gastric cancer, and may play a significant role in the development of aberrant promoter methylation during tumorigenesis.

Depletion of DNMT3A suppressed cell proliferation and restored PTEN in hepatocellular carcinoma cell.

Promoter hypermethylation mediated by DNA methyltransferases (DNMTs) is the main reason for epigenetic inactivation of tumor suppressor genes (TSGs). Previous studies showed that DNMT1 and DNMT3B play an important role in CpG island methylation in tumorigenesis. Little is known about the role of DNMT3A in this process, especially in hepatocellular carcinoma (HCC). In the present study, increased DNMT3A expression in 3 out of 6 HCC cell lines and 16/25 (64%) HCC tissues implied that DNMT3A is involved in hepatocellular carcinogenesis. Depletion of DNMT3A in HCC cell line SMMC-7721 inhibited cell proliferation and decreased the colony formation (about 65%). Microarray data revealed that 153 genes were upregulated in DNMT3A knockdown cells and that almost 71% (109/153) of them contain CpG islands in their 5' region. 13 of them including PTEN, a crucial tumor suppressor gene in HCC, are genes involved in cell cycle and cell proliferation. Demethylation of PTEN promoter was observed in DNMT3A-depleted cells implying that DNMT3A silenced PTEN via DNA methylation. These results provide insights into the mechanisms of DNMT3A to regulate TSGs by an epigenetic approach in HCC.

A functional polymorphism in the DNA methyltransferase-3A promoter modifies the susceptibility in gastric cancer but not in esophageal carcinoma.

BACKGROUND: DNA-methyltransferase (DNMT)-3A plays an important role in the development of embryogenesis and the generation of aberrant methylation in carcinogenesis. The aim of this study was to investigate the role of a DNMT3A promoter genetic variant on its transcriptional activity and to evaluate the association between DNMT3A gene polymorphism and the susceptibility to gastric cancer (GC) and oesophagus carcinoma (EC) in the Chinese population. METHODS: We selected one of the single nucleotide polymorphisms (SNPs) -448A>G in the DNMT3A promoter region and evaluated its effect on activity using a luciferase assay. -448A>G polymorphisms of DNMT3A were determined by polymerase chain reaction/restriction fragment length polymorphism and confirmed by sequencing. The distribution of -448A>G polymorphisms was detected in 208 GC patients and 346 healthy controls matched for age and gender. The distribution of -448A>G polymorphisms was also detected in 96 EC patients and matched 241 healthy controls. The association of -448A>G polymorphisms of DNMT3A and the risk of GC and EC was evaluated by stratified analysis according to the patient's age and gender. RESULTS: In a promoter assay, carriage of the -448 A allele showed a significantly higher promoter activity (> two fold) compared with the -448G allele (P < 0.001). The allele frequency of -448A among GC patients and controls was 32.9% versus 19.9%, respectively. Overall, we found that, compared with GG carriers, the DNMT3A -448AA homozygotes has a > six fold increased risk of GC. Stratification analysis showed that AA homozygotes have a more profound risk in the subgroups of individuals at the age range G polymorphism is a novel functional SNP and contributes to its genetic susceptibility to GC. -448A>G can be used as a stratification marker to predict an individual's susceptibility to GC, especially in the subgroups of individuals at the age range G in EC can not be used as a prediction marker in order to evaluate an individual's susceptibility to EC.

Expression pattern and clinical significance of DNA methyltransferase 3B variants in gastric carcinoma.

The aim of this study was to detect the expression pattern of DNA methyltransferase 3B (DNMT3B) variants in primary gastric cancer (GC) and to explore the clinical significance of DNMT3B variants in gastric carcinogenesis. Specific polymerase chain reaction (PCR) primer sets were designed to distinguish individual DNMT3B variants according to their splicing patterns. Expression levels of DNMT3B variants were assessed by quantitative real-time RT-PCR in gastric cancer tissue, normal gastric mucosae and GC cell lines. The relationship between the expression patterns of the DNMT3B variants and corresponding clinical information was analyzed by observing the expression levels of different variants in the tumors. These results demonstrate that DNMT3B overexpression is related to late phase invasion (P=0.029) and intestinal type (P=0.012) in GC. DNMT3B3 expression was higher in normal tissue, compared to tumor tissue (P=0.033). In contrast, only 18, 32 and 35% of the patient tumors overexpressed DNMT3B1, DNMT3B4 and DNMT3B5, respectively. While taking into account environmental factors (H. pylori, Epstein-Barr virus infection), H. pylori infection elevated DNMT3B1 and DNMT3B3 variants in tumors, while increasing DNMT3B4 in both tumor and non-cancerous tissues. Our findings indicated that the expression of DNMT3B3 is the major splice variant in normal gastric mucosae and may be affected by H. pylori infection. Elevated DNMT3B variants may influence the progression of gastric cancer and may possibly be a powerful indicator for the disease.

Overexpression of DNA methyltransferase 1 and its biological significance in primary hepatocellular carcinoma.

AIM: To explore the relationship between DNA methyltransferase 1 (DNMT1) and hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) and its biological significance in primary HCC. METHODS: We carried out an immunohistochemical examination of DNMT1 in both HCC and paired non-neoplastic liver tissues from Chinese subjects. DNMT1 mRNA was further examined in HCC cell lines by real-time PCR. We inhibited DNMT1 using siRNA and detected the effect of depletion of DNMT1 on cell proliferation ability and cell apoptosis in the HCC cell line SMMC-7721. RESULTS: DNMT1 protein expression was increased in HCCs compared to histologically normal non-neoplastic liver tissues and the incidence of DNMT1 immunoreactivity in HCCs correlated significantly with poor tumor differentiation (P = 0.014). There were more cases with DNMT1 overexpression in HCC with HBV (42.85%) than in HCC without HBV (28.57%). However, no significant difference in DNMT1 expression was found in HBV-positive and HBV-negative cases in the Chinese HCC group. There was a trend that DNMT1 RNA expression increased more in HCC cell lines than in pericarcinoma cell lines and normal liver cell lines. In addition, we inhibited DNMT1 using siRNA in the SMMC-7721 HCC cell line and found depletion of DNMT1 suppressed cells growth independent of expression of proliferating cell nuclear antigen (PCNA), even in HCC cell lines where DNMT1 was stably decreased. CONCLUSION: The findings implied that DNMT1 plays a key role in HBV-related hepatocellular tumorigenesis. Depletion of DNMT1 mediates growth suppression in SMMC-7721 cells.

2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism.

In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.