Advancing mortality rate prediction in European population clusters: integrating deep learning and multiscale analysis.

Accurately predicting population mortality rates is crucial for effective retirement insurance and economic policy formulation. Recent advancements in deep learning time series forecasting (DLTSF) have led to improved mortality rate predictions compared to traditional models like Lee-Carter (LC). This study focuses on mortality rate prediction in large clusters across Europe. By utilizing PCA dimensionality reduction and statistical clustering techniques, we integrate age features from high-dimensional mortality data of multiple countries, analyzing their similarities and differences. To capture the heterogeneous characteristics, an adaptive adjustment matrix is generated, incorporating sequential variation and spatial geographical information. Additionally, a combination of graph neural networks and a transformer network with an adaptive adjustment matrix is employed to capture the spatiotemporal features between different clusters. Extensive numerical experiments using data from the Human Mortality Database validate the superiority of the proposed GT-A model over traditional LC models and other classic neural networks in terms of prediction accuracy. Consequently, the GT-A model serves as a powerful forecasting tool for global population studies and the international life insurance field.

Combining biomarkers of BNIP3 L, S100B, NSE, and accessible measures to predict sepsis-associated encephalopathy: a prospective observational study.

BACKGROUND: Accurate identification of delirium in sepsis patients is crucial for guiding clinical diagnosis and treatment. However, there are no accurate biomarkers and indicators at present. We aimed to identify which combinations of cognitive impairment-related biomarkers and other easily accessible assessments best predict delirium in sepsis patients. METHODS: One hundred and one sepsis patients were enrolled in a prospective study cohort. S100B, NSE, and BNIP3 L biomarkers were detected in plasma and cerebrospinal fluid and patients' optic nerve sheath diameter (ONSD). The optimal biomarkers identified by Logistic regression are combined with other factors such as ONSD to filter out the perfect model to predict delirium in sepsis patients through Logistic regression, Naïve Bayes, decision tree, and neural network models. MAIN RESULTS: Among all biomarkers, compared with BNIP3 L (AUC = .706, 95% CI = .597-.815) and NSE (AUC = .711, 95% CI = .609-.813) in cerebrospinal fluid, plasma S100B (AUC = .729, 95% CI = .626-.832) had the best discrimination performance for delirium in sepsis patients. Logistic regression analysis showed that the combination of cerebrospinal fluid BNIP3 L with plasma S100B, ONSD, neutrophils, and age provided the best discrimination to cognitive impairment in sepsis patients (accuracy = .901, specificity = .923, sensitivity = .911), which was better than Naïve Bayes, decision tree, and neural network models. Neutrophils, ONSD, and cerebrospinal fluid BNIP3 L were consistently the major contributors in a few models. CONCLUSIONS: The logistic regression showed that the combination model was strongly correlated with cognitive dysfunction in sepsis patients.

Association of the High-Sensitivity C-Reactive Protein-to-Albumin Ratio with Carotid Atherosclerotic Plaque: A Community-Based Cohort Study.

Background: The inflammatory response is a pivotal factor in accelerating the progression of atherosclerosis. The high-sensitivity C-reactive protein-to-albumin ratio (CAR) has emerged as a novel marker of systemic inflammation. However, few studies have shown the CAR to be a promising prognostic marker for carotid atherosclerotic disease. This study aimed to analyse the predictive role of the CAR in carotid atherosclerotic disease. Methods: This community-based cohort study recruited 2003 participants from the Rose asymptomatic IntraCranial Artery Stenosis (RICAS) study who were free of stroke or transient ischemic attack. Carotid atherosclerotic plaques and their stability were identified via carotid ultrasound. Logistic regression models were utilized to investigate the association between CAR and the presence of carotid atherosclerotic plaques. Results: The prevalence of carotid atherosclerotic plaques was 38.79% in this study. After adjusting for clinical risk factors, including sex, age, dyslipidemia, hypertension, diabetes mellitus (DM), and smoking and drinking habits, a high CAR-level was independently associated with carotid plaque (odds ratio [OR] of upper: 1.46, 95% confidence interval [CI]: 1.13-1.90, P = 0.004; P for trend = 0.011). The highest CAR tertile was still significantly associated with carotid plaques among middle-aged (40-64 years) or female participants. Notably, an elevated CAR may be an independent risk factor for vulnerable carotid plaques (OR of upper: 2.06, 95% CI: 1.42-2.98, P < 0.001; P for trend <0.001). Conclusion: A high CAR may be correlated with a high risk of carotid plaques, particularly among mildly aged adults (40-64 years) or females. Importantly, the CAR may be associated with vulnerable carotid plaques, suggesting that the CAR may be a new indicator for stroke prevention.

Differential diagnosis of Crohn's disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning.

BACKGROUND: Crohn's disease (CD) is often misdiagnosed as intestinal tuberculosis (ITB). However, the treatment and prognosis of these two diseases are dramatically different. Therefore, it is important to develop a method to identify CD and ITB with high accuracy, specificity, and speed. AIM: To develop a method to identify CD and ITB with high accuracy, specificity, and speed. METHODS: A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis. RESULTS: The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB. CONCLUSION: Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

Phosphatidic acid inhibits SCAB1-mediated F-actin bundling in Arabidopsis.

Stomatal closure-associated actin-binding protein 1 (SCAB1) regulates stomatal closure by mediating actin filament reorganization in Arabidopsis thaliana. Our previous study showed that phosphatidylinositol 3-phosphate (PI3P) binds to SCAB1 and inhibits its oligomerization, thereby inhibiting its activity on F-actin in guard cells during stomatal closure. In this study, we show that another phospholipid, phosphatidic acid (PA), also binds to SCAB1 and inhibits its actin-bundling activity but not its actin-binding activity. F-actin bundling was promoted in vivo by treating Col-0 seedlings with n-butanol, a suppressor of PA production, but this effect was absent in the scab1 mutant. These results indicate that the signaling molecule PA is involved in the modulation of SCAB1 activity in F-actin reorganization.

Natural grasping movement recognition and force estimation using electromyography.

Electromyography (EMG) generated by human hand movements is usually used to decode different action types with high accuracy. However, the classifications of the gestures rarely consider the impact of force, and the estimation of the grasp force when performing natural grasping movements is so far overlooked. Decoding natural grasping movements and estimating the force generated by the associated movements can help patients to improve the accuracy of prosthesis control. This study mainly focused on two aspects: the classification of four natural grasping movements and the force estimation of these actions. For this purpose, we designed an experimental platform where subjects could perform four common natural grasping movements in daily life, including pinch, palmar, twist, and plug grasp, to complete target profiles. On the one hand, the results showed that, for natural grasping movements with different levels of force (three levels at 20, 50, and 80%), the average accuracy could reach from 91.43 to 97.33% under five classification schemes. On the other hand, the feasibility of force estimation for natural grasping movements was demonstrated. Furthermore, in the process of force estimation, we confirmed that the regression performance about plug grasp was the best, and the average R 2 could reach 0.9082. Besides, we found that the regression results were affected by the speed of force application. These findings contribute to the natural control of myoelectric prosthesis and the EMG-based rehabilitation training system, improving the user's experience and acceptance.

An Adaptive Deep Learning Optimization Method Based on Radius of Curvature.

An adaptive clamping method (SGD-MS) based on the radius of curvature is designed to alleviate the local optimal oscillation problem in deep neural network, which combines the radius of curvature of the objective function and the gradient descent of the optimizer. The radius of curvature is considered as the threshold to separate the momentum term or the future gradient moving average term adaptively. In addition, on this basis, we propose an accelerated version (SGD-MA), which further improves the convergence speed by using the method of aggregated momentum. Experimental results on several datasets show that the proposed methods effectively alleviate the local optimal oscillation problem and greatly improve the convergence speed and accuracy. A novel parameter updating algorithm is also provided in this paper for deep neural network.

Physical and Psychoacoustic Characteristics of Typical Noise on Construction Site: "How Does Noise Impact Construction Workers' Experience?"

Construction noise is an integral part of urban social noise. Construction workers are more directly and significantly affected by construction noise. Therefore, the construction noise situation within construction sites, the acoustic environment experience of construction workers, and the impact of noise on them are highly worthy of attention. This research conducted a 7-month noise level (LAeq) measurement on a construction site of a reinforced concrete structure high-rise residential building in northern China. The noise conditions within the site in different spatial areas and temporal stages was analyzed. Binaural recording of 10 typical construction noises, including earthwork machinery, concrete machinery, and hand-held machinery, were performed. The physical acoustics and psychoacoustic characteristics were analyzed with the aid of a sound quality analysis software. A total of 133 construction workers performing 12 types of tasks were asked about their subjective evaluation of the typical noises and given a survey on their noise experience on the construction site. This was done to explore the acoustic environment on the construction site, the environmental experience of construction workers, the impact of noise on hearing and on-site communications, and the corresponding influencing factors. This research showed that the noise situation on construction sites is not optimistic, and the construction workers have been affected to varying degrees in terms of psychological experience, hearing ability, and on-site communications. Partial correlation analysis showed that the construction workers' perception of noise, their hearing, and their on-site communications were affected by the noise environment, which were correlated to varying degrees with the individual's post-specific noise, demand for on-site communications, and age, respectively. Correlation analysis and cluster analysis both showed that the annoyance caused by typical construction noise was correlated to its physical and psychoacoustic characteristics. To maintain the physical and mental health of construction workers, there is a need to improve on the fronts of site management, noise reduction, equipment and facility optimization, and occupational protection.

Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models.

ABSTRACT: This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models.

Comparison between synthetic and rosemary-based antioxidants for the deep frying of French fries in refined soybean oils evaluated by chemical and non-destructive rapid methods.

Using natural antioxidants instead of synthetics ones has been the tendency for retarding the oil deterioration during repeated deep frying process. Concerning this, the comparison between synthetic tertiarybutyl hydroquinone (TBHQ) and rosemary-based antioxidants in frying French fries was hereby evaluated. The quality and stability of frying oils with rosemary-based antioxidants showed higher efficiency than TBHQ regarding oxidation parameters (i.e., chemical indices, sensory, etc.), where rosmarinic acid (RA) was the most effective, followed by rosemary extracts (RE) and carnosic acid (CA). LF-NMR results were highly correlated (R2 = 0.909-0.998) to the change in physicochemical properties tested, where RA could effectively regulate the relaxation spectrum (T2) change and decrease single component relaxation time (T2W). The PCA graph of NIRS also revealed the dynamic change of antioxidant effectiveness in accordance with that obtained by chemical methods. Hence, both LF-NMR and NIRS can be expected as rapid and efficient methods for future monitoring the frying process.

Blood-based FTIR-ATR spectroscopy coupled with extreme gradient boosting for the diagnosis of type 2 diabetes: A STARD compliant diagnosis research.

Timely diagnosis of type 2 diabetes and early intervention and treatment of it are important for controlling metabolic disorders, delaying and reducing complications, reducing mortality, and improving quality of life. Type 2 diabetes was diagnosed by Fourier transform mid-infrared (FTIR) attenuated total reflection (ATR) spectroscopy in combination with extreme gradient boosting (XGBoost). Whole blood FTIR-ATR spectra of 51 clinically diagnosed type 2 diabetes and 55 healthy volunteers were collected. For the complex composition of whole blood and much spectral noise, Savitzky-Golay smoothing was first applied to the FTIR-ATR spectrum. Then PCA was used to eliminate redundant data and got the best number of principle components. Finally, the XGBoost algorithm was used to discriminate the type 2 diabetes from healthy volunteers and the grid search algorithm was used to optimize the relevant parameters of the XGBoost model to improve the robustness and generalization ability of the model. The sensitivity of the optimal XGBoost model was 95.23% (20/21), the specificity was 96.00% (24/25), and the accuracy was 95.65% (44/46). The experimental results show that FTIR-ATR spectroscopy combined with XGBoost algorithm can diagnose type 2 diabetes quickly and accurately without reagents.

Detection of adulteration in Chinese honey using NIR and ATR-FTIR spectral data fusion.

The aim of this study is to find a fast, accurate, and effective method for the detection of adulteration in honey circulating in the market. Near-infrared spectroscopy and mid-infrared spectroscopy data on natural honey and syrup-adulterated honey were integrated in the experiment. A method for identifying natural honey and syrup-adulterated honey was established by incorporating these data into a Support Vector Machine (SVM). In this experiment, 112 natural pure honey samples of 20 common honey types from 10 provinces in China were collected, and 112 adulterated honey samples with different percentages of syrup (10, 20, 30, 40, 50, and 60%) were prepared using six types of syrup commonly used in industry. The total number of samples was 224. The near- and mid-infrared spectral data were obtained for all samples. The raw spectra were pre-processed by First Derivative (FD) transform, Second Derivative (SD) transform, Multiple Scattering Correction (MSC), and Standard Normal Variate Transformation (SNVT). The above-corrected data underwent low-level and intermediate-level data fusion. In the last step, Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed as the optimization algorithms to find the optimal penalty parameter c and the optimal kernel parameter g for the SVM, and to establish the best SVM-based detection model for natural honey and syrup-adulterated honey. The results reveal that, compared to low-level data fusion, intermediate-level data fusion significantly improves the detection model. With the latter, the accuracy, sensitivity and specificity of the optimal SVM model all reach 100%, which makes it ideal for the identification of natural honey and syrup-adulterated honey.

Determination of 10-Hydroxy-2-Decenoic Acid of Royal Jelly Using Near-Infrared Spectroscopy Combined with Chemometrics.

A rapid quantitative analysis model for determining the hydroxy-2-decenoic acid (10-HDA) content of royal jelly based on near-infrared spectroscopy combining with PLS has been developed. Firstly, near-infrared spectra of 232 royal jelly samples with different 10-HDA concentrations (0.35% to 2.44%) were be collected. Second-order derivative processing of the spectra was carried out to construct a full-spectrum PLS model. Secondly, GA-PLS, CARS-PLS, and Si-PLS were used to select characteristic wavelengths from the second-order derivative spectrum to construct a PLS calibration model. Finally, 58 samples were used to select the best predictive model for 10-HDA content. The result show that the PLS model constructed after wavelength selection was significantly more accurate than the full spectrum model. The Si-PLS algorithm performed best and the corresponding characteristic wavelength range were: 980 to 1038, 1220 to 1278, 1340 to 1398, and 1688 to 1746 nm. The prediction results were RMSEP = 0.1496% and RP = 0.9380. Hence, it is feasible to employ near-infrared spectra to analyze 10-HDA in royal jelly.

Alkyl and aromatic nitrates in atmospheric particles determined by gas chromatography tandem mass spectrometry.

Organic nitrates in the atmosphere are associated with photochemical pollution and are the main components of secondary organic aerosols, which are related to haze. An efficient method for determining organic nitrates in atmospheric fine particles (PM2.5) was established using synthesized standards. Four alkyl (C7-C10) nitrates and three aromatic nitrates (tolyl nitrate, phenethyl nitrate, and p-xylyl nitrate) were synthesized and characterized by 1H and 13C nuclear magnetic resonance spectroscopy. The optimal ions for quantifying and confirming the identities of the analytes were identified by analyzing the standards by gas chromatography tandem mass spectrometry. The tandem mass spectrometer was a triple quadrupole instrument. This method can obtain more accurate information of organic nitrates than on-line methods. Spiked recovery tests were performed using three spike concentrations, and the recoveries were 61.0-111.4 %, and the relative standard deviations were < 8.2% for all of the analytes. Limits of detection and quantification were determined, and the linearity of the method for each analyte was assessed. The applicability of the method was demonstrated by analyzing six PM2.5 samples. Overall, 87% of the analytes were detected in the samples. Phenethyl nitrate, heptyl nitrate, and octyl nitrate were detected in every sample. Phenethyl nitrate was found at a higher mean concentration (3.23 ng/m3) than the other analytes.

Early diagnosis of gastric cancer based on deep learning combined with the spectral-spatial classification method.

The development of an objective and rapid method that can be used for the early diagnosis of gastric cancer has important clinical application value. In this study, the fluorescence hyperspectral imaging technique was used to acquire fluorescence spectral images. Deep learning combined with spectral-spatial classification methods based on 120 fresh tissues samples that had a confirmed diagnosis by histopathological examinations was used to automatically identify and extract the "spectral + spatial" features to construct an early diagnosis model of gastric cancer. The model results showed that the overall accuracy for the nonprecancerous lesion, precancerous lesion, and gastric cancer groups was 96.5% with specificities of 96.0%, 97.3%, and 96.7% and sensitivities of 97.0%, 96.3%, and 96.6%, respectively. Therefore, the proposed method can increase the diagnostic accuracy and is expected to be a new method for the early diagnosis of gastric cancer.

Diagnosis of early gastric cancer based on fluorescence hyperspectral imaging technology combined with partial-least-square discriminant analysis and support vector machine.

This study investigated the feasibility of using fluorescence hyperspectral imaging technology to diagnose of early-stage gastric cancer. Fluorescence spectral images of 76 patients who were pathologically diagnosed as non-atrophic gastritis, premalignant lesions and gastric cancer were collected. Fluorescence spectra at 100-pixel points were randomly extracted after binarization. Diagnostic models of non-atrophic gastritis, premalignant lesions and gastric cancer were constructed through partial-least-square discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms. The prediction effects of PLS-DA and SVM models were compared. Results showed that the average spectra of normal, precancerous and gastric cancer tissues significantly differed at 496, 546, 640 and 670 nm, and regular changes in fluorescence intensity at 546 nm were in the following order: normal > precancerous lesions > gastric cancer. Additionally, the effect of the diagnostic model established by SVM is significantly better than PLS-DA which accuracy, specificity and sensitivity are above 94%. Experimental results revealed that the fast diagnostic model of early gastric cancer by combining fluorescence hyperspectral imaging technology and improved SVM was effective and feasible, thereby providing an accurate and rapid method for diagnosing early-stage gastric cancer.

Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM.

A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly, the real GA content in human serum was determined by GA enzymatic method, meanwhile, the ATR-FTIR spectra of serum samples from the population of health examination were obtained. The spectral data of the whole spectra mid-infrared region (4000-600 cm-1) and GA's characteristic region (1800-800 cm-1) were used as the research object of quantitative analysis. Secondly, several preprocessing steps including first derivative, second derivative, variable standardization and spectral normalization, were performed. Lastly, quantitative analysis regression models were established by using SiPLS and SVM respectively. The SiPLS modeling results are as follows: root mean square error of cross validation (RMSECVT) = 0.523 g/L, calibration coefficient (RC) = 0.937, Root Mean Square Error of Prediction (RMSEPT) = 0.787 g/L, and prediction coefficient (RP) = 0.938. The SVM modeling results are as follows: RMSECVT = 0.0048 g/L, RC = 0.998, RMSEPT = 0.442 g/L, and Rp = 0.916. The results indicated that the model performance was improved significantly after preprocessing and optimization of characteristic regions. While modeling performance of nonlinear SVM was considerably better than that of linear SiPLS. Hence, the quantitative analysis model for GA in human serum based on ATR-FTIR combined with SiPLS and SVM is effective. And it does not need sample preprocessing while being characterized by simple operations and high time efficiency, providing a rapid and accurate method for GA content determination.

A highly stereoselective organocatalytic tandem aminoxylation/aza-Michael reaction for the synthesis of tetrahydro-1,2-oxazines.

A facile stereoselective synthesis of multifunctionalized tetrahydro-1,2-oxazines (THOs) has been achieved by the organocatalyzed asymmetric tandem alpha-aminoxylation/aza-Michael reaction for the C-O/C-N bond formations in moderate to good yields with excellent diastereo- (>99:1 dr) and enantioselectivities (92% to >99% ee).

Microfluidic handling of PCR solution and DNA amplification on a reaction chamber array biochip.

A microfluidic biochip for conducting an array of polymerase chain reaction (PCR) simultaneously was fabricated to understand the microfluidic loading process of PCR solution into microfabricated glass reaction chambers. The geometrical factors of the microfluidic structure, including the shape and depth of the microchamber, shape and size of the microchannels were investigated on the formation of air bubbles trapped within the microchamber during the PCR solution loading process. Furthermore, the effects of surface properties of the microfluidic structure, including hydrophilicity of the microchamber and inlet channel, and hydrophobicity of the outlet channel, on the loading of PCR solution, especially on the formation of air bubbles were studied. As a result, the surface wetting property of the microchamber was found to be the main reason for the formation of the air bubbles inside the microchamber during the loading of PCR solution in the biochips. A solution to avoid the air trapping has been proposed and investigated.