Search Results (16)

Purpose: Hemodynamic response, particularly increased heart rate (HR) and blood pressure, can occur during tracheal intubation and is an adverse event to be avoided. The aim of this study was to use a network meta-analysis (NMA) to develop a ranking of hemodynamic responses (HR and mean blood pressure, MBP) after intubation of indirect and direct laryngoscopes in pediatric patients. Method: Studies were eligible for inclusion if they had a prospective randomized design, compared hemodynamic response (HR and MBP) to tracheal intubation between indirect and/or direct laryngoscopes, and were conducted in pediatric patients. The pooled difference between each intubation device’s intubation time is expressed as a weighted mean difference (WMD) of a 95% confidence interval (CI). The intubation time of the device was evaluated using P-scores calculated from the network point estimates and standard errors. A random-effects model was used when pooling effect sizes. We also analyzed intubation time as a related factor to hemodynamic responses. Results: From the electronic databases, we selected 16 trials for review. In a Macintosh-referenced analysis, Airtraq suppressed an increase of HR and MBP during tracheal intubation in pediatric patients significantly more than a Macintosh laryngoscope. (HR; WMD = −16.7, 95%CI −22.5 to −10.9, MBP; WMD = −8.57, 95%CI −10.9 to −6.27). Airtraq also topped the HR and MBP P-score rankings. The results of this study showed similar laryngoscopes in the top five rankings of P-scores (Airtraq, Coopdech video laryngoscope, Miller, C-MAC, Wis-Hipple) for HR and intubation time. Conclusions: We applied a network meta-analysis to create a consistent ranking of intubation devices that prevent hemodynamic changes during tracheal intubation in pediatric patients. In this NMA, Airtraq proved to be the best laryngoscope for preventing hemodynamic responses during tracheal intubation in pediatric patients. In the analysis of intubation time, Airtraq showed the shortest intubation time. Full article

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time–frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for the fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate Attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate Attention mechanism for time–frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time–frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed-signal time–frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification. Full article

Ear images are easy to capture, and ear features are relatively stable and can be used for identification. The ear images are all asymmetric, and the asymmetry of the ear images collected in the unconstrained environment will be more pronounced, increasing the recognition difficulty. Most recognition methods based on hand-crafted features perform poorly in terms of recognition performance in the face of ear databases that vary significantly in terms of illumination, angle, occlusion, and background. This paper proposes a feature fusion human ear recognition method based on channel features and dynamic convolution (CFDCNet). Based on the DenseNet-121 model, the ear features are first extracted adaptively by dynamic convolution (DY_Conv), which makes the ear features of the same class of samples more aggregated and different types of samples more dispersed, enhancing the robustness of the ear feature representation. Then, by introducing an efficient channel attention mechanism (ECA), the weights of important ear features are increased and invalid features are suppressed. Finally, we use the Max pooling operation to reduce the number of parameters and computations, retain the main ear features, and improve the model’s generalization ability. We performed simulations on the AMI and AWE human ear datasets, achieving 99.70% and 72.70% of Rank-1 (R1) recognition accuracy, respectively. The recognition performance of this method is significantly better than that of the DenseNet-121 model and most existing human ear recognition methods. Full article

Background: Dietary patterns play a critical role in diabetes management, while the best dietary pattern for Type 2 diabetes (T2DM) patients is still unclear. The aim of this network meta-analysis was to compare the impacts of various dietary approaches on the glycemic control of T2DM patients. Methods: Relevant studies were retrieved from PubMed, Embase, Web of Knowledge, Cochrane Central Register of Controlled Trials (CENTRAL), and other additional records (1949 to 31 July 2022). Eligible RCTs were those comparing different dietary approaches against each other or a control diet in individuals with T2DM for at least 6 months. We assessed the risk of bias of included studies with the Cochrane risk of bias tool and confidence of estimates with the Grading of Recommendations Assessment, Development, and Evaluation approach for network meta-analyses. In order to determine the pooled effect of each dietary approach relative to each other, we performed a network meta-analysis (NMA) for interventions for both HbA1c and fasting glucose, which enabled us to estimate the relative intervention effects by combing both direct and indirect trial evidence. Results: Forty-two RCTs comprising 4809 patients with T2DM were included in the NMA, comparing 10 dietary approaches (low-carbohydrate, moderate-carbohydrate, ketogenic, low-fat, high-protein, Mediterranean, Vegetarian/Vegan, low glycemic index, recommended, and control diets). In total, 83.3% of the studies were at a lower risk of bias or had some concerns. Findings of the NMA revealed that the ketogenic, low-carbohydrate, and low-fat diets were significantly effective in reducing HbA1c (viz., −0.73 (−1.19, −0.28), −0.69 (−1.32, −0.06), and −1.82 (−2.93, −0.71)), while moderate-carbohydrate, low glycemic index, Mediterranean, high-protein, and low-fat diets were significantly effective in reducing fasting glucose (viz., −1.30 (−1.92, −0.67), −1.26 (−2.26, −0.27), −0.95 (−1.51, −0.38), −0.89 (−1.60, −0.18) and −0.75 (−1.24, −0.27)) compared to a control diet. The clustered ranking plot for combined outcomes indicated the ketogenic, Mediterranean, moderate-carbohydrate, and low glycemic index diets had promising effects for controlling HbA1c and fasting glucose. The univariate meta-regressions showed that the mean reductions of HbA1c and fasting glucose were only significantly related to the mean weight change of the subjects. Conclusions: For glycemic control in T2DM patients, the ketogenic diet, Mediterranean diet, moderate-carbohydrate diet, and low glycemic index diet were effective options. Although this study found the ketogenic diet superior, further high-quality and long-term studies are needed to strengthen its credibility. Full article

Sound event detection (SED) is the task of finding the identities of sound events, as well as their onset and offset timings from audio recordings. When complete timing information is not available in the training data, but only the event identities are known, SED should be solved by weakly supervised learning. The conventional U-Net with global weighted rank pooling (GWRP) has shown a decent performance, but extensive computation is demanded. We propose a novel U-Net with limited upsampling (LUU-Net) and global threshold average pooling (GTAP) to reduce the model size, as well as the computational overhead. The expansion along the frequency axis in the U-Net decoder was minimized, so that the output map sizes were reduced by 40% at the convolutional layers and 12.5% at the fully connected layers without SED performance degradation. The experimental results on a mixed dataset of DCASE 2018 Tasks 1 and 2 showed that our limited upsampling U-Net (LUU-Net) with GTAP was about 23% faster in training and achieved 0.644 in audio tagging and 0.531 in weakly supervised SED tasks in terms of F1 scores, while U-Net with GWRP showed 0.629 and 0.492, respectively. The major contribution of the proposed LUU-Net is the reduction in the computation time with the SED performance being maintained or improved. The other proposed method, GTAP, further improved the training time reduction and provides versatility for various audio mixing conditions by adjusting a single hyperparameter. Full article

Characterized by its resilience, connectivity, and real-time data processing capabilities, the fourth industrial revolution, referred to as Industry 4.0, is the main driver of today’s digital transformation. It is crucially important for manufacturing facilities to correctly identify the most suitable Industry 4.0 technologies that meet their operational schemes and production targets. Different technology selection frameworks were proposed to tackle this problem, several of which are complex, or require historic data from manufacturing facilities that might not always be available. The aim of this paper is to develop a novel Industry 4.0 selection framework that utilizes Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) to rank different Industry 4.0 technologies based on their economic, social, and environmental impact. The framework is also implemented on a real-life case study of a manufacturing firm to rank the different Industry 4.0 technologies required for its digital transformation based on their significance to the facility’s key performance indicators. The framework is utilized to select the top three Industry 4.0 technologies from a pool of eight technologies that are deemed important to the manufacturing firm. Results of the case study showed that Cyber-Physical Systems, Big Data analytics, and autonomous/industrial robots are the top three ranked technologies, having closeness coefficient scores of 0.964, 0.928, and 0.601, respectively. Moreover, the framework showed sensitivity towards weight changes. This is an advantage in the developed framework, since its main aim is to provide policymakers with a customized list of technologies based on their importance to the firm. Full article

Background and Objectives: Ipsilateral shoulder pain (ISP) is a common complication after thoracic surgery. Severe ISP can cause ineffective breathing and impair shoulder mobilization. Both phrenic nerve block (PNB) and suprascapular nerve block (SNB) are anesthetic interventions; however, it remains unclear which intervention is most effective. The purpose of this study was to compare the efficacy and safety of PNB and SNB for the prevention and reduction of the severity of ISP following thoracotomy or video-assisted thoracoscopic surgery. Materials and methods: Studies published in PubMed, Embase, Scopus, Web of Science, Ovid Medline, Google Scholar and the Cochrane Library without language restriction were reviewed from the publication’s inception through 30 September 2022. Randomized controlled trials evaluating the comparative efficacy of PNB and SNB on ISP management were selected. A network meta-analysis was applied to estimate pooled risk ratios (RRs) and weighted mean difference (WMD) with 95% confidence intervals (CIs). Results: Of 381 records screened, eight studies were eligible. PNB was shown to significantly lower the risk of ISP during the 24 h period after surgery compared to placebo (RR 0.44, 95% CI 0.34 to 0.58) and SNB (RR 0.43, 95% CI 0.29 to 0.64). PNB significantly reduced the severity of ISP during the 24 h period after thoracic surgery (WMD −1.75, 95% CI −3.47 to −0.04), but these effects of PNB were not statistically significantly different from SNB. When compared to placebo, SNB did not significantly reduce the incidence or severity of ISP during the 24 h period after surgery. Conclusion: This study suggests that PNB ranks first for prevention and reduction of ISP severity during the first 24 h after thoracic surgery. SNB was considered the worst intervention for ISP management. No evidence indicated that PNB was associated with a significant impairment of postoperative ventilatory status. Full article

Multi-modal fusion can exploit complementary information from various modalities and improve the accuracy of prediction or classification tasks. In this paper, we propose a parallel, multi-modal, factorized, bilinear pooling method based on a semi-tensor product (STP) for information fusion in emotion recognition. Initially, we apply the STP to factorize a high-dimensional weight matrix into two low-rank factor matrices without dimension matching constraints. Next, we project the multi-modal features to the low-dimensional matrices and perform multiplication based on the STP to capture the rich interactions between the features. Finally, we utilize an STP-pooling method to reduce the dimensionality to get the final features. This method can achieve the information fusion between modalities of different scales and dimensions and avoids data redundancy due to dimension matching. Experimental verification of the proposed method on the emotion-recognition task using the IEMOCAP and CMU-MOSI datasets showed a significant reduction in storage space and recognition time. The results also validate that the proposed method improves the performance and reduces both the training time and the number of parameters. Full article

Increased yield can be achieved by optimising the growth environment, improving the plant gene pool, or a combination of the two. This study’s objective was to evaluate the effect of combined heat and water stress (CHWS) on maize yield, grown in various soil conditions. The experimental design was a four-replicated 3 × 3 × 2 × 3 factorial in a completely randomized design. Three water stress levels, three soil amendments, two soil textural types, and three drought-tolerant maize varieties were combined to create 54 treatment interactions. The result showed that as the severity of the water stress increased, the yield decreased. The near terminal water stress reduced cob weight, grain weight, and grain number by 96, 97, and 97%, respectively. The maize varieties were ranked WE5323 ≥ ZM1523 > WE3128 in terms of average performance and stability. Under heat and moderate water stress, the poultry manure amendment performed well for WE5323 and ZM1523, while the mineral fertilizer amendment performed best for WE3128. Compared to the inorganic amendment, the organic had a greater ameliorative capacity for grain yield under CHWS. For improved grain yield under CHWS, farmers are advised to grow WE5323 and ZM1523 with organic amendments. The findings in this study could improve food security strategies for low-income households living in high-stress environments. Full article

Exposure to PM_2.5 has been associated with the prevalence of obesity. In the Greater Mexico City Area (GMCA), both are ranked among the highest in the world. Our aim was to analyze this association in children, adolescents, and adults in the GMCA. We used data from the 2006 and 2012 Mexican National Surveys of Health and Nutrition (ENSANUT). Participants’ past-year exposure to ambient PM_2.5 was assessed using land use terms and satellite-derived aerosol optical depth estimates; weight and height were measured. We used survey-adjusted logistic regression models to estimate the odds ratios (ORs) of obesity (vs. normal-overweight) for every 10 µg/m³ increase in annual PM_2.5 exposure for children, adolescents, and adults. Using a meta-analysis approach, we estimated the overall odds of obesity. We analyzed data representing 19.3 million and 20.9 million GMCA individuals from ENSANUT 2006 and 2012, respectively. The overall pooled estimate between PM_2.5 exposure and obesity was OR = 1.96 (95% CI: 1.21, 3.18). For adolescents, a 10 µg/m³ increase in PM_2.5 was associated with an OR of 3.53 (95% CI: 1.45, 8.58) and 3.79 (95% CI: 1.40, 10.24) in 2006 and 2012, respectively. More studies such as this are recommended in Latin American cities with similar air pollution and obesity conditions. Full article

The paper proposes an effective continuous gesture recognition method, which includes two modules: segmentation and recognition. In the segmentation module, the video frames are divided into gesture frames and transitional frames by using the information of hand motion and appearance, and continuous gesture sequences are segmented into isolated sequences. In the recognition module, our method exploits the spatiotemporal information embedded in RGB and depth sequences. For the RGB modality, our method adopts Convolutional Long Short-Term Memory Networks to learn long-term spatiotemporal features from short-term spatiotemporal features obtained from a 3D convolutional neural network. For the depth modality, our method converts a sequence into Dynamic Images and Motion Dynamic Images through weighted rank pooling and feed them into Convolutional Neural Networks, respectively. Our method has been evaluated on both ChaLearn LAP Large-scale Continuous Gesture Dataset and Montalbano Gesture Dataset and achieved state-of-the-art performance. Full article

Diets could play an important role in testicular function, but studies on how adherence to the dietary patterns influences human testicular function in Asian countries are scarce. Herein, we examined the association between testosterone-related dietary patterns and testicular function among adult men in Taiwan. This cross-sectional study recruited 3283 men who attended a private medical screening program from 2009 to 2015. Testosterone-related dietary pattern was generated by the reduced rank regression (RRR) method. The association between adherence to quartile of dietary pattern scores with sex hormones (testosterone, follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2)) and sperm quality (sperm concentration (SC), total sperm motility (TSM), progressive motility (PRM), and normal sperm morphology (NSM)) were examined by multivariable linear regression. Hemoglobin (β = 0.57, p < 0.001), hematocrit (β = 0.17, p = 0.002), triglyceride (β = −0.84, p < 0.001), HDL-cholesterol (β = 3.58, p < 0.001), total cholesterol to HDL-cholesterol ratio (β = −0.78, p < 0.001), and uric acid (β = −10.77, p < 0.001) were highly correlated with testosterone levels. Therefore, these biomarkers were used to construct a testosterone-related dietary pattern. Highest adherence (Q4) to dietary pattern scores were negatively associated with lower testosterone in the pooled analysis (β = −0.89, p = 0.037) and normal-weight men (β = −1.48, p = 0.019). Likewise, men in the Q4 of the dietary pattern had lower SC (β = −5.55, p = 0.001) and NSM (β = −2.22, p = 0.007) regardless of their nutritional status. Our study suggesting that testosterone-related dietary pattern (rich in preserved vegetables or processed meat or fish, deep-fried foods, innards organs, rice or flour products cooked in oil, and dipping sauce, but low in milk, dairy products, legumes, or beans, and dark or leafy vegetables) was associated with a poor testicular function. Full article

Minimum dietary diversity for women of reproductive age (MDD-W) was validated as a population-level proxy of micronutrient adequacy, with indicator data collection proposed as either list-based or open recall. No study has assessed the validity of these two non-quantitative proxy methods against weighed food records (WFR). We assessed the measurement agreement of list-based and open recall methods as compared to WFR (i.e., reference method of individual quantitative dietary assessment) for achieving MDD-W and an ordinal food group diversity score. Applying a non-inferiority design, data were collected from non-pregnant women of reproductive age in Cambodia (n = 430), Ethiopia (n = 431), and Zambia (n = 476). For the pooled sample (n = 1337), proportions achieving MDD-W from both proxy methods were compared to WFR proportion by McNemar’s chi-square tests, Cohen’s kappa, and receiver operating characteristic (ROC) analysis. Ordinal food group diversity (0–10) was compared by Wilcoxon matched-pairs signed-rank tests, intraclass correlation coefficients (ICC), and weighted kappa. MDD-W food groups that were most frequently misreported (i.e., type I and II errors) by the proxy methods were determined. Our findings indicate statistically significant differences in proportions achieving MDD-W, ordinal food group diversity scores, and ROC curves between both proxy methods and WFR (p < 0.001). List-based and open recall methods overreported women achieving MDD-W by 16 and 10 percentage points, respectively, as compared to WFR (proportion achieving MDD-W: 30%). ICC values between list-based or open recall and WFR were 0.50 and 0.55, respectively. Simple and weighted kappa values both indicated moderate agreement between list-based or open recall against WFR. Food groups most likely to be misreported using proxy methods were beans and peas, dark green leafy vegetables, vitamin A-rich fruit and vegetables, and other fruits. Our study provides statistical evidence for overreporting of both list-based and open recall methods for assessing prevalence of MDD-W or ordinal food group diversity score in women of reproductive age in low- and middle-income countries. Operationalizing MDD-W through qualitative recall methods should consider potential trade-offs between accuracy and simplicity. Full article

The paper presents a novel hybrid network for large-scale action recognition from multiple modalities. The network is built upon the proposed weighted dynamic images. It effectively leverages the strengths of the emerging Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches to specifically address the challenges that occur in large-scale action recognition and are not fully dealt with by the state-of-the-art methods. Specifically, the proposed hybrid network consists of a CNN based component and an RNN based component. Features extracted by the two components are fused through canonical correlation analysis and then fed to a linear Support Vector Machine (SVM) for classification. The proposed network achieved state-of-the-art results on the ChaLearn LAP IsoGD, NTU RGB+D and Multi-modal & Multi-view & Interactive ( $M^{2}I$ ) datasets and outperformed existing methods by a large margin (over 10 percentage points in some cases). Full article

Background and objectives: Identification of cancer biomarkers that are differentially expressed (DE) between two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, those are not suitable for paired samples. Furthermore, the traditional methods use p-values or fold change (FC) values to detect the DE genes. However, sometimes, p-value based results do not comply with FC based results due to the smaller pooled variance of gene expressions, which occurs when variance of each individual condition becomes smaller. There are some methods that combine both p-values and FC values to solve this problem. But, those methods also show weak performance for small sample cases in the presence of outlying expressions. To overcome this problem, in this paper, an attempt is made to propose a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values computed by SAM statistic using minimum β-divergence method, which is designed for paired samples. Materials and Methods: The proposed method introduces a weight function known as β-weight function. This weight function produces larger weights corresponding to usual and smaller weights for unusual expressions. The β-weight function plays the significant role on the performance of the proposed method. The proposed method uses β-weight function as a measure of outlier detection by setting β = 0.2. We unify both classical and robust estimates using β-weight function, such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum β-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. Results: We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP, and FCROS) using both simulated and real gene expression profiles for both small and large sample cases. From the simulation and a real spike in data analysis results, we observed that the proposed method outperforms other methods for small sample cases in the presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP, and FCROS) otherwise. From the head and neck cancer (HNC) gene expression dataset, the proposed method identified two additional genes (CYP3A4 and NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan–Meier curve revealed that combined effect of these two genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank literature based gene associations. Conclusions: Using pathway analysis, disease association study, protein–protein interactions and survival analysis we found that our proposed two additional genes might be involved in the critical pathways of cancer. Furthermore, the identified drugs showed statistical significance which indicates that proteins associated with these genes might be therapeutic target in cancer. Full article