Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To close this gap, a two-stage deep neural network (TDNN) is proposed. In particular, in the first stage, using the rated essays for nontarget prompts as the training data, a shallow model is learned to select essays with an extreme quality for the target prompt, serving as pseudo training data; in the second stage, an end-to-end hybrid deep model is proposed to learn a prompt-dependent rating model consuming the pseudo training data from the first step. Evaluation of the proposed TDNN on the standard ASAP dataset demonstrates a promising improvement for the prompt-independent AES task.
ObjectiveLymph node (LN) metastasis is widely accepted as a poor prognosis indicator in patients with gastric cancer. An accurate preoperative prediction of LN status is of crucial importance for the planning treatment. The aim of the present study was to assess the predictive value of the preoperative platelet/lymphocyte (PLR) and neutrophil/lymphocyte rates (NLR) on the LN metastasis in gastric cancer patients and to develop a new preoperative score system to predict LN metastasis.Patients and methodsA total of 492 operable patients with gastric cancer were enrolled in our study. The clinical utility of the PLR and NLR was evaluated by receiver operating characteristic (ROC) curves. The logistic analysis was used to identify the independent parameters associated with LN metastasis. Then, a score system including those independent parameters that can be detected preoperatively was established, which was also tested by an ROC curve.ResultsThe ideal cutoff values for predicting LN metastasis were 1.59 for NLR and 155.67 for PLR according to the ROC curve. Multivariate analyses showed that both PLR and NLR are significantly associated with LN metastasis independent of depth of invasion, lymphatic invasion, macroscopic type, and tumor size. The area under the ROC curve of the score system was 0.830 (95% confidence interval 0.782–0.878), showing a reliable ability to evaluate the status of nodal involvement.ConclusionPreoperative PLR and NLR are useful biomarkers to predict LN metastasis and the score system in our study may serve as a reliable instrument to predict LN metastasis in gastric cancer patients.
ObjectiveMindfulness-based interventions have been widely demonstrated to be effective in reducing stress, alleviating mood disorders, and improving quality of life; however, the underlying mechanisms remained to be fully understood. Along with the advanced research in the microbiota-gut-brain axis, this study aimed to explore the impact of gut microbiota on the effectiveness and responsiveness to mindfulness-based cognitive therapy (MBCT) among high trait anxiety populations.DesignA standard MBCT was performed among 21 young adults with high trait anxiety. A total of 29 healthy controls were matched for age and sex. The differences in gut microbiota between the two groups were compared. The changes in fecal microbiota and psychological indicators were also investigated before and after the intervention.ResultsCompared with healthy controls, we found markedly decreased bacterial diversity and distinctive clusters among high trait anxiety populations, with significant overgrowth of bacteria such as Streptococcus, Blautia, and Romboutsia, and a decrease in genera such as Faecalibacterium, Coprococcus_3, and Lachnoclostridium. Moreover, MBCT attenuated trait anxiety and depression, improved mindfulness and resilience, and increased the similarity of gut microbiota to that of healthy controls. Notably, a high presence of intestinal Subdoligranulum pre-MBCT was associated with increased responsiveness to MBCT. Decreases in Subdoligranulum post-MBCT were indicative of ameliorated trait anxiety. The tryptophan metabolism pathways were significantly over-represented among high responders compared to low responders.ConclusionThe significantly increased diversity post-MBCT added evidence to gut-brain communication and highlighted the utility of mycobiota-focused strategies for promoting the effectiveness and responsiveness of the MBCT to improve trait anxiety.Clinical Trial Registrationchictr.org.cn, ChiCTR1900028389.
The accuracy of attitude and heading measurement, as well as the system real-time performance are basic indicators used to evaluate an attitude and heading reference system (AHRS). In order to improve the attitude and heading measurement accuracy under dynamic complex environment, the AHRS system should also have numerical stability and calculation robustness. The AHRS system based on MEMS multi-sensor fusion can realize fusion processing of data measured by multiple sensors, so as to calculate and obtain the optimal carrier attitude and heading information, conduct real-time output, and improve the accuracy and reliability of attitude and heading measurement. For the AHRS system consisting of MEMS gyroscope, accelerometer and triaxial magnetometer, attitude and heading detection principle and algorithm based on MEMS multi-sensor fusion were proposed in this study: The information of the system itself was firstly used to discriminate motion state of the carrier within the filtering cycle, and then Kalman filtering was conducted using different measured information according to motion state to correct the attitude error angle caused by gyroscopic drift. On this basis, an attitude fusion algorithm based on extended Kalman filtering technology was designed for time update process of Kalman filtering, output information of accelerometer was taken as observed quantity under certain conditions to realize measurement updating process of Kalman filtering, and then attitude angle was calculated. In an optical fiber attitude and heading system project in practical engineering, a vehicle field test analysis was carried out simultaneously with the system using ordinary attitude algorithm, and the results showed that the extended Kalman filtering algorithm designed according to the simulation results could realize multi-sensor information fusion, improve measurement accuracy and realize accurate attitude positioning, so as to provide simpler and more flexible criteria for carrier motion status. The results have verified the accuracy and reliability of the algorithm, so it is feasible in practical engineering.
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