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Abstract Chronic kidney disease (CKD) is often a common comorbidity in critically ill patients with type 2 diabetes mellitus (T2DM). This study explored the relationship between blood urea nitrogen to serum albumin ratio (BAR) and mortality in T2DM patients with CKD in intensive care unit (ICU). Patients were recruited from the Medical Information Mart database, retrospectively. The primary and secondary outcomes were 90-day mortality, the length of ICU stay, hospital mortality and 30-day mortality, respectively. Cox regression model and Kaplan–Meier survival curve were performed to explore the association between BAR and 90-day mortality. Subgroup analyses were performed to determine the consistency of this association. A total of 1920 patients were enrolled and divided into the three groups (BAR 21.3). The length of ICU stay, 30-day mortality, and 90-day mortality in the BAR > 21.3 group were significantly higher than other groups. In Cox regression analysis showed that high BAR level was significantly associated with increased greater risk of 90-day mortality. The adjusted HR (95%CIs) for the model 1, model 2, and model 3 were 1.768 (1.409–2.218), 1.934, (1.489–2.511), and 1.864, (1.399–2.487), respectively. Subgroup analysis also showed the consistency of results. The Kaplan–Meier survival curve analysis revealed similar results as well that BAR > 21.3 had lower 90-day survival rate. High BAR was significantly associated with increased risk of 90-day mortality. BAR could be a simple and useful prognostic tool in T2DM patients with CKD in ICU.

Background Back-projection has been used to correct the variance and polarity indeterminacies for the independent component analysis. The variance and polarity of the components are essential features of neuroscience studies.Objective This work extends the back-projection theory to canonical polyadic decomposition (CPD) for high-order tensors, aiming to correct the variance and polarity indeterminacies of the components extracted by CPD.Methods The tensor is reshaped into a matrix and decomposed using a suitable blind source separation algorithm. Subsequently, the coefficients are projected using back-projection theory, and other factor matrices are computed through a series of singular value decompositions of the back-projection matrix.Results By applying this method, the energy and polarity of each component are determined, effectively correcting the variance and polarity indeterminacies in CPD. The proposed method was validated using simulated tensor data and resting-state fMRI data.Conclusion Our proposed back-projection method for high-order tensors effectively corrects variance and polarity indeterminacies in CPD, offering a precise solution for calculating the energy and polarity required to extract meaningful features from neuroimaging data.

Abstract Text-to-speech synthesis plays an essential role in facilitating human-computer interaction. Currently, the predominant approach in Text-to-speech acoustic models selects only the Mel spectrum as an intermediate feature for converting text to speech. However, the Mel spectrograms obtained may exhibit ambiguity in some aspects owing to the limited capability of the Fourier transform to capture mutation signals during the acquisition of the Mel spectrograms. With the aim of improving the clarity of synthesized speech, this study proposes a multi-task learning optimization method and conducts experiments on the Tacotron2 speech synthesis system to demonstrate the effectiveness of the proposed method. The method in the study introduces an additional task: wavelet spectrograms. The continuous wavelet transform has gained significant popularity in various applications, including speech enhancement and speech recognition, which is primarily attributed to its capability to adaptively vary the time-frequency resolution and its excellent performance in capturing non-stationary signals. This study highlights that the clarity of Tacotron2 synthesized speech can be improved by introducing Wavelet-spectrogram as an auxiliary task through theoretical and experimental analysis: a feature extraction network is added, and Wavelet-spectrogram features are extracted from the Mel spectrum output generated by the decoder. Experimental findings indicate that the Mean Opinion Score achieved for the speech synthesized by the model using multi-task learning is 0.17 higher compared to the baseline model. Furthermore, by analyzing the factors contributing to the success of the continuous wavelet transform-based multi-task learning method in the Tacotron2 model, as well as the effectiveness of multi-task learning, the study conjectures that the proposed method has the potential to enhance the performance of other acoustic models.

Abstract The role of inflammation and the correlation between inflammatory markers and type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) have been studied. In clinical work, a large number of T2DM patients complicated with CKD, but the cause of CKD was not clear. Our study aimed to evaluate the relationship between monocyte-to-lymphocyte ratio (MLR) and mortality in T2DM patients with CKD. The data from Medical Information Mart for Intensive Care III was analyzed. The primary outcome was 90-day all-cause mortality; the secondary outcomes were the length of ICU stay, hospital mortality and 30-day all-cause mortality. Cox regression was used to evaluate the association between MLR and 90-day mortality. We performed subgroup analyses to determine the consistency of this association, and used Kaplan–Meier survival curve to analysis the survival of different levels of MLR. A total of 1830 patients were included in study retrospectively. The length of ICU stay, 30-day all-cause mortality, and 90-day all-cause mortality in the MLR > 0.71 group were significantly higher than those in the MLR 0.71 had worst prognosis. In T2DM patients with CKD in the intensive care unit, high MLR was significantly associated with increased risk 90-day all-cause mortality.

Abstract Background There is no predictive tool for type 2 diabetes mellitus (T2DM) patients with acute kidney injury (AKI). Our study aimed to establish an effective nomogram model for predicting mortality in T2DM patients with AKI. Method Data on T2DM patients with AKI were obtained from the Medical Information Mart for Intensive Care III. 70% and 30% of the patients were randomly selected as the training and validation cohorts, respectively. Univariate and multivariate logistic regression analyses were used to identify factors associated with death in T2DM patients with AKI. Factors significantly associated with survival outcomes were used to construct a nomogram predicting 90-day mortality. The nomogram effect was evaluated by receiver operating characteristic curve analysis, Hosmer‒Lemeshow test, calibration curve, and decision curve analysis (DCA). Results There were 4375 patients in the training cohort and 1879 in the validation cohort. Multivariate logistic regression analysis showed that age, BMI, chronic heart failure, coronary artery disease, malignancy, stages of AKI, white blood cell count, blood urea nitrogen, arterial partial pressure of oxygen and partial thromboplastin time were independent predictors of patient survival. The results showed that the nomogram had a higher area under the curve value than the sequential organ failure assessment score and simplified acute physiology score II. The Hosmer‒Lemeshow test and calibration curve suggested that the nomogram had a good calibration effect. The DCA curve showed that the nomogram model had good clinical application value. Conclusion The nomogram model accurately predicted 90-day mortality in T2DM patients with AKI. It may provide assistance for clinical decision-making and treatment, thereby reducing the medical burden.

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. TCA then reveals spatially and temporally shared components, i.e., evoked networks with the naturalistic stimuli, their time courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with the adaptive TCA algorithm, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of the TCA algorithm. We demonstrated the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also applied the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are evoked by naturalistic movie viewing.

Abstract Background Nonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, the performance of their use in EEG data analysis has not been fully compared. Here, we provide a comparison of four NMF algorithms in terms of accuracy of estimation, stability (repeatability of the results) and time complexity of algorithms with simulated data. In the practical application of NMF algorithms, stability plays an important role, which was an emphasis in the comparison. A Hierarchical clustering algorithm was implemented to evaluate the stability of NMF algorithms. Results In simulation-based comprehensive analysis of fit, stability, accuracy of estimation and time complexity, hierarchical alternating least squares (HALS) low-rank NMF algorithm (lraNMF_HALS) outperformed the other three NMF algorithms. In the application of lraNMF_HALS for real resting-state EEG data analysis, stable and interpretable features were extracted. Conclusion Based on the results of assessment, our recommendation is to use lraNMF_HALS, providing the most accurate and robust estimation.

Objective To establish the relationship between pulse wave transit time (PWTT) before anaesthesia induction and blood pressure variability (BPV) during anaesthesia induction. Methods This prospective observational cohort study enrolled consecutive patients that underwent elective surgery. Invasive arterial pressure, electrocardiography, pulse oximetry, heart rate and bispectral index were monitored. PWTT and BPV were measured with special software. Anaesthesia was induced with propofol, sufentanil and rocuronium. Results A total of 54 patients were included in this study. There was no correlation between BPV and the dose of propofol, sufentanil and rocuronium during anaesthesia induction. Bivariate linear regression analysis demonstrated that PWTT ( r = –0.54), age ( r = 0.34) and systolic blood pressure ( r = 0.31) significantly correlated with systolic blood pressure variability (SBPV). Only PWTT ( r = –0.38) was significantly correlated with diastolic blood pressure variability (DBPV). Patients were stratified into high PWTT and low PWTT groups according to the mean PWTT value (96.8 ± 17.2 ms). Compared with the high PWTT group, the SBPV of the low PWTT group increased significantly by 3.4%. The DBPV of the low PWTT group increased significantly by 2.1% compared with the high PWTT group. Conclusions PWTT, assessed before anaesthesia induction, may be an effective predictor of haemodynamic fluctuations during anaesthesia induction.

In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because large-scale networks are widely spatially distributed and thus have increased mutual information with noise. As such, conventional ICA algorithms with high model orders may not extract these components at all. This conflict makes the selection of model order a problem. We present a new strategy for model order free ICA, called Snowball ICA, that obviates these issues. The algorithm collects all information for each network from fMRI data without the limitations of network scale. Using simulations and in vivo resting-state fMRI data, our results show that component estimation using Snowball ICA is more accurate than traditional ICA. The Snowball ICA software is available at https://github.com/GHu-DUT/Snowball-ICA.

This paper presents a new method of injection-production allocation estimation for water-flooding mature oilfields. The suggested approach is based on logistic growth rate functions and several type-curve matching methods. Using the relationship between these equations, oil production and water injection rate as well as injection-production ratio can be easily forecasted. The calculation procedure developed and outlined in this paper requires very few production data and is easily implemented. Furthermore, an oilfield case has been analyzed. The synthetic and field cases validate the calculation procedure, so it can be accurately used in forecasting production data, and it is important to optimize the whole injection-production system.

A triple band-notched ultrawide band (UWB) antenna is presented to avoid the interference of services working in the UWB band, such as WLAN, WiMAX and X-band satellite systems. The arc H-shaped slot on the radiating patch creates a low frequency notched band, while the other two band-notched bands are formed by cutting narrow slots on the ground plane. The presented antenna can operate on the ultrawide band efficiently and inhibit interference from three different kinds of narrow band communication systems. The simulation and measurement results show that the antenna has excellent band-notched function on the rejectband and almost omnidirectional radiation pattern on the passband.

In order to incorporate different communication standards into a single device, a compact quad-band slot antenna is proposed in this paper. The proposed antenna is composed of a dielectric substrate, T-shaped microstrip patch with a circle slot and an inverted L-slot, and a comb-shaped ground on the back of the substrate. By adopting these structures, it can produce four different bands, while maintaining a small size and a simple structure. Furthermore, a prototype of the quad-band antenna is designed and fabricated. The simulated and measured results show that the proposed antenna can operate over the 1.79–2.63 GHz, 3.46–3.97 GHz, 4.92–5.85 GHz, and 7.87–8.40 GHz, which can cover entire PCS (Personal Communications Service, 1.85–1.99 GHz), UMTS (Universal Mobile Telecommunications System, 1.92–2.17 GHz), WCDMA (wideband code-division multiple access, 2.1 GHz), Bluetooth (2.4–2.48 GHz), WiBro (Wireless Broad band access service, 2.3–3.39 GHz), WLAN (Wireless Local Area Networks, 2.4/5.2/5.8 GHz), WiMAX (Worldwide Interoperability for Microwave Access, 2.5/3.5/5.5 GHz), and X-band SATcom applications (7.9~8.4 GHz). The proposed antenna is particularly attractive for mobile devices integrating multiple communication systems.

The aim of this research was to produce novel antitumor fluoroquinolones from antibacterial analogs by introduction of a heterocyclic ring as a bioisostere of the C-3 carboxylic acid group. To this end, two series of eleven s-triazole derivatives bearing functionalized side chains of Schiff bases and Schiff–Mannich bases were designed and synthesized. Structures were characterized by elemental analysis and spectral data after which in vitro antitumor activity against L1210, CHO and HL60 cell lines was evaluated in the MTT assay. Compounds possessing a free phenol group were particularly active and warrant further development.