Effects of Linpan nature therapy on health benefits in older women with and without hypertension.

Background: Nature therapy can significantly benefit the physiology and psychology of middle-aged and older people, but previous studies have focused on forest environments. The restoration potential of rural environments in urban fringe areas, which are more accessible to older people on a daily basis, has not been fully studied. This study assessed the effects of nature therapy on the physical and mental health of older women in a rural setting (locally known as Linpan) in the urban fringe area of Chengdu, China. Methods: We recruited a total of 60 older women (65.3 ± 5.5 years old) living in cities for 3 days of nature therapy in the winter (30 subjects) and spring (30 subjects), including 20 hypertensive patients. Results: The results showed that the overall blood pressure, pulse and sleep dysfunction rating scores of the participants were significantly lower than the pretest levels, and the finger blood oxygen saturation, mid-day salivary alpha-amylase and cortisol were increased post-treatment. Increases in these biomarker indicates and increase in stress. There were significant differences in the changes in systolic blood pressure between the hypertension group (HTN) and the normal group (normal) (HTN decreased by 8.8%, normal decreased by 5.4%), salivary alpha-amylase content (HTN decreased by 0.3%, normal increased by 16.9%), and sleep dysfunction rating scores (HTN decreased by 59.6%, normal decreased by 54%). The decreases in systolic blood pressure and pulse in the winter group were higher than those in the spring group by 1.8 and 4.4%, respectively, while the increases in salivary alpha-amylase content and salivary cortisol content were lower than those in the spring group by 11.7 and 11.2%, respectively, and the decrease in sleep dysfunction rating scores was lower than that in the spring group by 7.1%. Conclusion: Our study concluded that nature therapy based on various health activities in the Linpan has significant health effects on older women. It can regulate blood pressure and pulse in older women, relieve cardiovascular disease, improve sleep quality. Meanwhile, older women with high blood pressure experienced a more significant effect than the healthy group.

Detraque: Dynamic execution tracing techniques for automatic fault localization of hardware design code.

In an error-prone development process, the ability to localize faults is a crucial one. Generally speaking, detecting and repairing errant behavior at an early stage of the development cycle considerably reduces costs and development time. The debugging of the Verilog program takes much time to read the waveform and capture the signal, and in many cases, problem-solving relies heavily on experienced developers. Most existing Verilog fault localization methods utilize the static analysis method to find faults. However, using static analysis methods exclusively may result in some types of faults being inevitably ignored. The use of dynamic analysis could help resolve this issue. Accordingly, in this work, we propose a new fault localization approach for Verilog, named Detraque. After obtaining dynamic execution through test cases, Detraque traces these executions to localize faults; subsequently, it can determine the likelihood of any Verilog statement being faulty and sort the statements in descending order by suspicion score. Through conducting empirical research on real Verilog programs with 61 faulty versions, Detraque can achieve an EXAM score of 18.3%. Thus, Detraque is verified as able to improve Verilog fault localization effectiveness when used as a supplement to static analysis methods.

Convolutional neural networks-based health risk modelling of some heavy metals in a soil-rice system.

The long-term consumption of heavy metal-rich rice can cause serious harm to human health. However, the existing health risk assessment (HRA) can only be performed after the rice has been harvested, and this approach belongs to a passive and lagging pattern. This study is the first to explore the feasibility of health risk (HR) prediction by proposing the indirect model CNNHR-IND and the direct model CNNHR-DIR based on the convolutional neural network (CNN) technology. The dataset included 390 pairs of soil-rice samples collected from You County, China, with 17 environmental covariates. The R2 values for CNNHR-IND for non-carcinogenic and carcinogenic risks were 0.578 and 0.554, respectively, and those for CNNHR-DIR were 0.647 and 0.574, respectively. The results demonstrated that both models performed well, especially CNNHR-DIR had a higher estimation accuracy. The spatial autocorrelation analysis indicated that CNNHR-DIR exerted no systematic bias in the prediction results for health risks, confirming the rationality of the CNNHR-DIR model. The sensitivity analysis further confirmed the generalizability and robustness of CNNHR-DIR. This study proved the feasibility of HR prediction and the potential of CNN technology in HRA, and is significant regarding early risk warnings of rice planting and the sustainable development of public health.

A field study to estimate heavy metal concentrations in a soil-rice system: Application of graph neural networks.

Accurate prediction of the concentration of heavy metals is of great significance for assessing the quality of agricultural products and reducing health risks. However, the complexity and interconnectivity of the farmland ecosystem restricts the improvement of the prediction accuracy of traditional methods. This research explored the application potential of graph neural network (GNN) technology, which can extract and learn information in large-scale networks in detail, in the field of heavy metal prediction for the first time. In this study, a heavy metal prediction model for rice, CoNet-GNN, was proposed with 17 environmental factors as input variables using the co-occurrence network and GNN. Experimental results using a dataset from a field study showed that the R2 of CoNet-GNN for predicting Cd, Pb, Cr, As, and Hg had outstanding values of 0.872, 0.711, 0.683, 0.489, and 0.824, respectively. Sensitivity analysis further indicated that CoNet-GNN had good stability and robustness. Compared with random forest, gradient boosting, and multilayer perceptron, CoNet-GNN made a remarkable improvement to the prediction accuracy of all studied heavy metals. Therefore, CoNet-GNN can effectively simulate the rich relationships and laws between various factors in the soil-rice system and effectively characterize the influence diffusion path. Furthermore, it provides new ideas for heavy metal prediction based on network research methods and expands the technical scope of heavy metal evaluation.

Convolutional neural network-based applied research on the enrichment of heavy metals in the soil-rice system in China.

The enrichment of heavy metals in the soil-rice system is affected by various factors, which hampers the prediction of heavy metal concentrations. In this research, a prediction model (CNN-HM) of heavy metal concentrations in rice was constructed based on convolutional neural network (CNN) technology and 17 environmental factors. For comparison, other machine learning models, such as multiple linear regression, Bayesian ridge regression, support vector machine, and backpropagation neural networks, were applied. Furthermore, the LH-OAT method was used to evaluate the sensitivity of CNN-HM to each environmental factor. The results showed that the R2 values of CNN-HM for Cd, Pb, Cr, As, and Hg were 0.818, 0.709, 0.688, 0.462, and 0.816, respectively, and both the MAE and RMAE values were acceptable. The sensitivity analysis showed that the concentrations of Cd and Pb, mechanical composition, soil pH, and altitude were the main sensitive features for CNN-HM. Compared with CNN-HM based on all input features, the performance of the quick prediction model that was based on the sensitive features did not degrade significantly, thereby indicating that CNN-HM has stronger stability and robustness. The quick prediction model has extensive application value for timely prediction of the enrichment of heavy metals in emergencies. This study demonstrated the effectiveness and practicability of CNNs in predicting heavy metal enrichment in the soil-rice system and provided a new perspective and solution for heavy metal prediction.

Kaempferol inhibits gastric cancer tumor growth: An in vitro and in vivo study.

Kaempferol, which is one of the general flavonoids, has recently been reported to suppress proliferation, induce cell cycle arrest and promote apoptosis in various human cancer cell lines. In the present study, the effect and mechanism of kaempferol on gastric cancer (GC) was examined. The results showed that kaempferol significantly inhibited the proliferation of MKN28 and SGC7901 cell lines. However, no significant inhibition in the GSE-1 normal gastric epithelial cell line in our experimental dose was detected. Additionally, significant apoptosis and G2/M phase cell cycle arrest were identified following the treatment of kaempferol. More importantly, we observed that kaempferol inhibited the growth of the tumor xenografts although no marked effects on liver, spleen or body weight were induced. The expression levels of G2/M cell cycle­regulating factors, cyclin B1, Cdk1 and Cdc25C, were significantly reduced. In addition, kaempferol treatment markedly decreased the level of Bcl-2 concomitant with an increase in Bax expression, resulting in the upregulation of cleaved caspase-3 and -9, which promoted PARP cleavage. Kaempferol-treated cells also led to a decrease in p-Akt, p-ERK and COX-2 expression levels. The present study therefore provided evidence that kaempferol may be a therapeutic agent for GC.

Compositional mining of multiple object API protocols through state abstraction.

API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In this paper, we present a general, scalable, multiple object API protocols mining approach that can capture all object interactions. Our approach uses abstract field values to label object states during the mining process. We first mine single object typestates as finite state automata whose transitions are annotated with states of interacting objects before and after the execution of the corresponding method and then construct multiple object API protocols by composing these annotated single object typestates. We implement our approach for Java and evaluate it through a series of experiments.

Effects of recombinant human growth hormone on intestinal translocation of bacteria and endotoxin in rats with obstructive jaundice.

BACKGROUND: Extrahepatic biliary obstruction promotes intestinal translocation of bacteria and endotoxin and this process is an important cause of morbidity and mortality in patients with jaundice. This study was undertaken to investigate the effect and mechanism of recombinant human growth hormone(rhGH) and to alleviate intestinal translocation of bacteria and endotoxin in murine obstructive jaundice. METHODS: A group of 42 Wistar rats were divided into 3 groups:sham operation(SO), bile duct ligation (BDL), and BDL and rhGH treatment(rhGH). By the end of the experiment, on day 7, the animals were killed, and their liver function and serum endotoxin were measured, bacterial cultures of the liver, kidney and mesenchymal lymph were made. Terminal ileum mucosa was observed under an electron microscope. RESULTS: Liver function was improved more significantly in the rhGH group than in the BDL group. The value of endotoxin in the rhGH group was 0.38+/-0.03 EU/ml, significantly lower than that in the BDL group(0.65+/-0.04 EU/ml, P < 0.01), and similar to that in the SO group (0.30+/-0.02 EU/ml, P > 0.05). The rate of bacteria translocation in the liver, kidney and mesenteric lymph was much higher in the BDL group than in other two groups. The rate of bacteria translocation in mesenteric lymph was 64.29%, significantly higher than that in the SO group and the rhGH group (P < 0.05). There was no significant difference in bacteria translocation rate between the SO group and the rhGH group (P > 0.05). Under an electron microscope, ileum mucosa epithelial cells in the BDL group were necrotic, and organelle were markedly metamorphic. In the rhGH group, ultrastructural changes were less evident or similar to those in the SO group. CONCLUSION: rhGH has significant protective effects on intestinal mucosa barrier in obstructive jaundice, and reduces intestinal translocation of bacteria and endotoxin.