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Electrocardiography (ECG) is the gold standard for monitoring vital signs and for diagnosing, controlling, and preventing cardiovascular diseases (CVDs). However, ECG requires continuous user participation, and cannot be used for continuous cardiac monitoring. In contrast to ECG, photoplethysmography (PPG) devices do not require continued user involvement, and can offer ongoing and long-term detection capabilities. However, from a medical perspective, ECG can provide more information about the heart. Currently, most existing work contains different signals recorded from the same subject in training and test sets. This study proposes a neural network model based on a 1D convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. This neural network model can directly reconstruct ECG signals from PPG signals. The learned features are captured from the CNN model and fed into the BiLSTM model. In order to verify the validity of the model, it is evaluated using the MIMIC II dataset in the completely subject-independent model (records are placed in a training set, and a test set appears once, but the test signal belongs to a record that is not in the training set). The Pearson’s correlation coefficient between the reconstructed ECG and the reference ECG of the proposed model is 0.963 in the completely subject-independence model. The results of the proposed model are better than those of several cited state-of-the-art models. The results of our trained model indicate that we can obtain reconstructed ECGs that are highly similar to reference ECGs in the completely subject-independent model.

ECG helps in diagnosing heart disease by recording heart activity. During long-term measurements, data loss occurs due to sensor detachment. Therefore, research into the reconstruction of missing ECG data is essential. However, ECG requires user participation and cannot be used for continuous heart monitoring. Continuous monitoring of PPG signals is conversely low-cost and easy to carry out. In this study, a deep neural network model is proposed for the reconstruction of missing ECG signals using PPG data. This model is an end-to-end deep learning neural network utilizing WNet architecture as a basis, on which a bidirectional long short-term memory network is added in establishing a second model. The performance of both models is verified using 146 records from the MIMIC III matched subset. Compared with the reference, the ECG reconstructed using the proposed model has a Pearson’s correlation coefficient of 0.851, root mean square error (RMSE) of 0.075, percentage root mean square difference (PRD) of 5.452, and a Fréchet distance (FD) of 0.302. The experimental results demonstrate that it is feasible to reconstruct missing ECG signals from PPG.

Abstract Mg–15Gd–1Zn (wt.%) alloy was successfully prepared via the spark plasma sintering rapid solidification ribbons process. Microstructure investigation showed that the sintered alloys consisted of fine grains, the β1 phase, and long-perioded stacking ordered phase (LPSO). The sintering temperature and time have a significant effect on the microstructural evolution. A lower sintering temperature (430 °C ) was beneficial for obtaining finer grain sizes with less than 5 μm and a higher content of β1 phase with a content of 3–15 vol.% and a size-distribution of (10–600) nm. A higher temperature for a longer sintering time, 450–470 °C and 5–10 min, helpfully promoted precipitating the abundantly lamellar LPSO phase, and its content was 2–10 vol.% for LPSO phase with the width of (10–100) nm. The mechanical properties indicated that the fine grain size and supersaturated solid solution contributed at least 50% of the yield stress, and the residual contribution was related to the β1 phase and LPSO phase strengthening, which were based on their contents and the sizes.

The current high-salinity wastewater treatment technology is complex, costly, and carries the risk of secondary contamination. As a traditional desalination technology, the combined method using frozen technology has broad development prospects in wastewater treatment. This study investigates the desalination effects of waste leachate using three different methods: the frozen–gravity method (FGM), frozen–centrifugal method (FCM), and frozen–blowing methods (FBMs), under various experimental conditions. The results showed that the salt rejection of all three methods could reach more than 75% under the conditions of a freezing time of 12 h, freezing temperature of −15 °C, and ice production rate of 40%; the salt rejection of FGM increased at higher ambient temperatures, but it was not conducive to the removal of organic pollutants; the salt rejection of FCM was sensitive to the centrifugal time and centrifugal speed, with a significant correlation (p < 0.05), the increase in centrifugal time and centrifugal speed can help to improve the salt rejection, and the increase in centrifugal speed in the range of 1000–2000 rpm can accelerate the discharge of concentrated brine more effectively; the frozen–crushed–blowing method (FCBM) in FBM has a salt rejection as high as 93.86% at an ice production rate of 25.80%, which reduces the salinity of the effluent from 4.07% to 0.25%, speeds up the desalination process, and improves the salt rejection compared to the other methods. This study provides a new perspective and reference for the treatment of high-saline wastewater.

Electrocardiography and photoplethysmography are non-invasive techniques that measure signals from the cardiovascular system. While the cycles of the two measurements are highly correlated, the correlation between the waveforms has rarely been studied. Measuring the photoplethysmogram (PPG) is much easier and more convenient than the electrocardiogram (ECG). Recent research has shown that PPG can be used to reconstruct the ECG, indicating that practitioners can gain a deep understanding of the patients’ cardiovascular health using two physiological signals (PPG and ECG) while measuring only PPG. This study proposes a subject-based deep learning model that reconstructs an ECG using a PPG and is based on the bidirectional long short-term memory model. Because the ECG waveform may vary from subject to subject, this model is subject-specific. The model was tested using 100 records from the MIMIC III database. Of these records, 50 had a circulatory disease. The results show that a long ECG signal could be effectively reconstructed from PPG, which is, to our knowledge, the first attempt in this field. A length of 228 s of ECG was constructed by the model, which was trained and validated using 60 s of PPG and ECG signals. To segment the data, a different approach that segments the data into short time segments of equal length (and that do not rely on beats and beat detection) was investigated. Segmenting the PPG and ECG time series data into equal segments of 1-min width gave the optimal results. This resulted in a high Pearson’s correlation coefficient between the reconstructed 228 s of ECG and referenced ECG of 0.818, while the root mean square error was only 0.083 mV, and the dynamic time warping distance was 2.12 mV per second on average.

ORY-1001, an inhibitor of covalent lysine (K)-specific demethylase 1A (KDM1A), has been used as a therapy for the treatment of acute leukemia. However, the underlying mechanisms of anticancer are still not fully elucidated. Here, we report that KDM1A is highly expressed in lung cancers, where it appears to drive aggressive growth. Furthermore, lung cancer patients with higher KDM1A levels have worse survival outcomes than patients with lower KDM1A levels. Interestingly, ORY-1001significantly inhibited the cell proliferation, colony formation, cell cycle, and induced apoptosis, by regulating the Warburg effect through controlling Hexokinases 2 (HK2) expression. In summary, these results indicate that ORY-1001 could inhibit the growth of lung cancer cells via regulating the Warburg effect by controlling HK2.

Cisplatin (DDP) is currently one of the most commonly used chemotherapeutic drugs for treating ovarian and lung cancer. However, resistance to cisplatin is common and it often leads to therapy failure. In addition, the precise mechanism of cisplatin resistance is still in its infancy. In this study, we demonstrated that the oxidative pentose phosphate pathway enzyme 6-phosphogluconate dehydrogenase (6PGD) promotes cisplatin resistance. We showed that cisplatin-resistant cancer cells (C13∗ and A549DDP), had higher levels of 6PGD compared to their cisplatin-sensitive counterparts (OV2008 and A549). Furthermore, ovarian and lung cancer patients with higher 6PGD levels have worse survival outcomes relative to patients with lower 6PGD expression. Interestingly, we found that the upregulation of 6PGD in cisplatin-resistant cells was due to the decreased expression of miR-206 and miR-613, which we found to target this enzyme. We further demonstrate that suppressing 6PGD using shRNA, inhibitor or miR-206/miR-613, either as single agents or in combination, could sensitize cisplatin-resistant cancer cells to cisplatin treatment and thereby improving the therapeutic efficacy of cisplatin. Taken together, our results suggest that 6PGD serves as a novel potential target to overcome cisplatin resistance.

Aiming at the problem of limited field-of-view of air-to-air missile during cooperative attack, a cooperative guidance law with field-of-view angle and impact point attitude constraints is proposed. After the engagement geometric model between the missile and the maneuvering target is established in the vertical plane, the guidance law is designed. Firstly, based on the time-varying sliding mode surface, the guidance law of the normal direction of the line of sight is designed, and the integral obstacle Lyapunov function is selected to ensure that the missile can stably track the target during the attacking process, and the missile can attack the target at the expected impact point angle. The integral obstacle Lyapunov function is selected to prove the convergence of the guidance law. Secondly, on the basis of estimating the remaining time of the attack, a guidance law with time constraints is designed to ensure that the missile hits the target at the preset time. Finally, the feasibility of the guidance law is verified by simulation in three scenarios.