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Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method. •MCCA combines multiple data sets into a common representation.•MCCA can be used to summarize data across subjects.•MCCA can be used to denoise data, or reduce dimensionality, based on consistency across subjects.

Common carotid artery (CCA) diagnosis is very important for carrying out an assessment of the severity of vascular disease and being able to suggest treatment solutions, whether with careful surgical planning or even an interventional radiological surgery. Early diagnosis of carotid atherosclerosis is an essential step in preventing stroke from occurring. This is the motivation for us to develop a novel Computer-Aided Diagnosis (CAD) system for CCA disease diagnosis. Our novel CAD system contains four phases named: segmentation, localization, intima-media thickness (IMT) measurement, and classification of the CCA as normal and abnormal. Each phase in our integrated system has its role and novelty contribution that distinguishes it from any previous studies and researches. These roles and contributions of all phases will be discussed later in this paper. These phases have been applied for the CCA in transverse and longitudinal sections to help in the early diagnosis of atherosclerosis providing a complete diagnosis approach. The CCA has been localized in the transverse section images based on a deep learning technique called faster regional proposal convolutional neural network (Faster R-CNN). The IMT measurement of the CCA has been accomplished in a longitudinal section based on edge detection techniques. The CCA-lumen segmentation has been made in a longitudinal section using active contour criteria. The CCA longitudinal section has been classified as normal and abnormal using the transfer learning of the pre-trained convolutional neural network (CNN) called AlexNet. Experiments have been performed on three different ultrasound image datasets that were manually collected. The comparison between our suggested localization phase circles and the clinician’s delineations shows an average Jaccard similarity of 90.86% with an accuracy of 97.5%. The mean ± standard deviation (SD) of our method and the experts for IMT measurements are 0.7573 ± 0.52 mm and 0.7604 ± 0.52, respectively. The obtained classification results show 100% for specificity, sensitivity, and accuracy. These results, show the superiority of the proposed system over other systems in the literature.

Environmental factors play a fundamental role in shaping fish assemblage in aquatic ecosystems. The present study describes the fish assemblage structure on the spatial scale in Pong Reservoir, which lies in foothills of the Northwestern Himalaya within the river Beas basin. Through sophisticated enviro assessment techniques, using ArcGIS mapping, this study provides valuable insight into how physicochemical factors shape the fish assemblage in the reservoir. In total, 1211 individuals belonging to 8 orders, 10 families, 15 genera, and 19 species were recorded. The order Cypriniformes has the highest number of species. The invasive species Oreochromis niloticus was also documented for the first time in this reservoir. At a spatial scale, diversity indices reveal that the lacustrine zone had the lowest fish diversity. The transitional zone showed more species richness as compared to other riverine and lacustrine. The IUCN conservation status showed that among 19 fish species, two species (Wallago attu and Cyprinus carpio) are under vulnerable and only one species, Tor putitora, is under the endangered category. The majority of recorded water quality parameters fell within the acceptable range. CCA analysis of physiochemical parameters with fish abundance reveals that DO and turbidity were found to positively influence the species abundance. These findings provide a valuable resource for understanding ecology and also lay a solid foundation for the development of effective fisheries management strategies in this reservoir.

(OV)-associated cholangiocarcinoma (CCA) has a high immune response with chronic inflammation and oxidative stress. CD44 and Nestin, two cancer stem cell (CSC) markers, play major roles in cancer cell survival. Effects of immune response and expression CSC markers on survival of patients with CCA remain unclear. To investigate the effects of level of OV IgG together with CSC marker expression and also the combination of these markers on survival of CCA patients after curative resection. All serum specimens from CCA patients who underwent curative surgery from 2005 to 2015 were examined for IgG for OV antigen by ELISA. Tissue specimens were studied for CD44 and Nestin expression. Survival analysis by Cox proportional hazard model was used for estimating hazard ratio (HR) with a 95% confidence interval (CI). In this study, 122 (69.3%) of 176 were positive for OV IgG, and 35 (19.9%) were considered to have high-positive OV IgG. CD44s positive expression was found in 54 (40%), CD44v6 high expression in 96 (69.6%), CD44v8-10 high expression in 87 (63.5%) and Nestin high expression in 21 (16.1%). Multivariate survival analysis found that high-positive OV IgG and late stage tumor were independent prognostic factors with the adjusted HR of 2.24 (95% CI 1.27-3.93) and 2.78 (95% CI 1.46-5.29), respectively. Subgroup analysis in early and late stage CCA showed that a combined positive OV IgG and CD44s expression with the high expression of CD44v8-10 had the significantly poorest prognosis with HR of 3.75 (95% CI 1.61-8.72) and HR of 1.76 (95% CI 1.02-3.03), respectively. A high level of OV IgG as well as a high level of CSC markers resulted in an aggressive CCA. OV IgG level together with CSC markers can be used as the prognostic markers for CCA patients' survival. The study of the CD44 pathway is promising for adjuvant treatment.

Lake level changes are critical indicators of hydrological balance and climate change, yet long‐term monthly lake level reconstruction is challenging with incomplete or short‐term data. Data‐driven models, while promising, struggle with nonstationary lake level changes and complex dependencies on meteorological factors, limiting their applicability. Here, we introduce the Hydroformer, a frequency domain enhanced multi‐attention Transformer model designed for monthly lake level reconstruction, utilizing reanalysis data. This model features two innovative mechanisms: (a) Frequency‐Enhanced Attention (FEA) for capturing long‐term temporal dependence, and (b) Causality‐based Cross‐dimensional Attention (CCA) to elucidate how specific meteorological factors influence lake level. Seasonal and trend patterns of catchment meteorological factors and lake level are initially identified by a time series decomposition block, then independently learned and refined within the model. Tested across 50 lakes globally, the Hydroformer excelled in reconstruction periods ranging from half to three times the training‐test length. The model exhibited good performance even when training data missing rates were below 50%, particularly in lakes with significant seasonal fluctuations. The Hydroformer demonstrated robust generalization across lakes of varying sizes, from 10.11 to 18,135 km2, with median values for R2, MAE, MSE, and RMSE at 0.813, 0.313, 0.215, and 0.4, respectively. Furthermore, the Hydroformer outperformed data‐driven models, improving MSE by 29.2% and MAE by 24.4% compared to the next best model, the FEDformer. Our method proposes a novel approach for reconstructing long‐term water level changes and managing lake resources under climate change. Plain Language Summary Lake water levels, as key indicators of hydrologic dynamics and catchment balance, are vital for understanding climate impacts and managing water resources. However, the lack of continuous measurements for most global lakes, combined with the inability of traditional data‐driven models to effectively decipher complex interactions with catchment hydrological processes, leads to significant gaps in generalizability, accuracy, and reconstructive length. Given these limitations, accurate monthly reconstructions of lake level remain a persistent challenge. To address this, we develop Hydroformer, an innovative frequency domain enhanced multi‐attention Transformer model, utilizing reanalysis data for monthly lake level reconstruction. It employs two innovative attention mechanisms: Frequency‐Enhanced Attention for capturing long‐term temporal dependencies and Causality‐based Cross‐dimensional Attention for cross‐dimensional causal dependencies between catchment meteorological factors and lake level. Through a decomposition block, the model efficiently recognizes and refines inherent seasonal and trend patterns, leading to a comprehensive understanding of lake behaviors. Through testing on 50 global lakes, the Hydroformer has exhibited exceptional performance in reconstructing water levels for lakes ranging from 10.11 to 18,135 km2, adeptly handling short‐term, long‐term, and varying proportions of data gaps. It notably outperforms supervised data‐driven models. This positions it as a vital instrument for monthly lake level reconstruction, showcasing the power of integrating advanced artificial intelligence techniques in hydrological modeling. Key Points A novel frequency domain enhanced multi‐attention Transformer model, Hydroformer, has been built for reconstructing monthly lake level using reanalysis data The model accurately extends reconstructions 2–3 times the training data length, excelling with less than 50% missing training data Hydroformer surpasses advanced AI‐based models, improving MSE and MAE by over 20% and demonstrating strong generalization across lakes of varying sizes

Cholangiocarcinoma accounts for approximately 10% of all hepatobiliary tumors and represents 3% of all new-diagnosed malignancies worldwide. Intrahepatic cholangiocarcinoma (i-CCA) accounts for 10% of all cases, perihilar (h-CCA) cholangiocarcinoma represents two-thirds of the cases, while distal cholangiocarcinoma accounts for the remaining quarter. Originally described by Klatskin in 1965, h-CCA represents one of the most challenging tumors for hepatobiliary surgeons, mainly because of the anatomical vascular relationships of the biliary confluence at the hepatic hilum. Surgery is the only curative option, with the goal of a radical, margin-negative (R0) tumor resection. Continuous efforts have been made by hepatobiliary surgeons in order to achieve R0 resections, leading to the progressive development of aggressive approaches that include extended hepatectomies, associating liver partition, and portal vein ligation for staged hepatectomy, pre-operative portal vein embolization, and vascular resections. i-CCA is an aggressive biliary cancer that arises from the biliary epithelium proximal to the second-degree bile ducts. The incidence of i-CCA is dramatically increasing worldwide, and surgical resection is the only potentially curative therapy. An aggressive surgical approach, including extended liver resection and vascular reconstruction, and a greater application of systemic therapy and locoregional treatments could lead to an increase in the resection rate and the overall survival in selected i-CCA patients. Improvements achieved over the last two decades and the encouraging results recently reported have led to liver transplantation now being considered an appropriate indication for CCA patients.

Background Cognitive and neuropsychiatric impairment, known as cerebellar cognitive affective syndrome (CCAS), may be present in cerebellar disorders. This study identified distinct CCAS subtypes in cerebellar patients using cluster analysis. Methods The German CCAS-Scale (G-CCAS-S), a brief screening test for CCAS, was assessed in 205 cerebellar patients and 200 healthy controls. K-means cluster analysis was applied to G-CCAS-S data to identify cognitive clusters in patients. Demographic and clinical variables were used to characterize the clusters. Multiple linear regression quantified their relative contribution to cognitive performance. The ability of the G-CCAS-S to correctly distinguish between patients and controls was compared across the clusters. Results Two clusters explained the variance of cognitive performance in patients’ best. Cluster 1 (30%) exhibited severe impairment. Cluster 2 (70%) displayed milder dysfunction and overlapped substantially with that of healthy controls. Cluster 1 patients were on average older, less educated, showed more severe ataxia and more extracerebellar involvement than cluster 2 patients. The cluster assignment predicted cognitive performance even after adjusting for all other covariates. The G-CCAS-S demonstrated good discriminative ability for cluster 1, but not for cluster 2. Conclusions The variance of cognitive impairment in cerebellar disorders is best explained by one severely affected and one mildly affected cluster. Cognitive performance is not only predicted by demographic/clinical characteristics, but also by cluster assignment itself. This indicates that factors that have not been captured in this study likely have effects on cognitive cerebellar functions. Moreover, the CCAS-S appears to have a relative weakness in identifying patients with only mild cognitive deficits. Study registration The study has prospectively been registered at the German Clinical Study Register ( https://www.drks.de ; DRKS-ID: DRKS00016854).

This work aims to provide a novel multimodal sarcasm detection model that includes four stages: pre-processing, feature extraction, feature level fusion, and classification. The pre-processing uses multimodal data that includes text, video, and audio. Here, text is pre-processed using tokenization and stemming, video is pre-processed during the face detection phase, and audio is pre-processed using the filtering technique. During the feature extraction stage, such text features as TF-IDF, improved bag of visual words, n-gram, and emojis as well on the video features using improved SLBT, and constraint local model (CLM) are extraction. Similarly the audio features like MFCC, chroma, spectral features, and jitter are extracted. Then, the extracted features are transferred to the feature level fusion stage, wherein an improved multilevel canonical correlation analysis (CCA) fusion technique is performed. The classification is performed using a hybrid classifier (HC), e.g. bidirectional gated recurrent unit (Bi-GRU) and LSTM. The outcomes of Bi-GRU and LSTM are averaged to obtain an effective output. To make the detection results more accurate, the weight of LSTM will be optimally tuned by the proposed opposition learning-based aquila optimization (OLAO) model. The MUStARD dataset is a multimodal video corpus used for automated sarcasm discovery studies. Finally, the effectiveness of the proposed approach is proved based on various metrics.

With the development of information technology, it is an important issue to ensure the secure transmission and storage of data in today's society. An experimental and innovative encryption method based on selected ciphertext attacks and improved adversarial neural networks was proposed to improve the security performance of computer communication systems under various attack modes. The experiment conducted a comprehensive security analysis of the proposed encryption technology and verified its effectiveness in resisting different types of attack modes by simulating different attack scenarios. The results showed that in the comparison of convergence iterations of different models, when iterations were 38, the research model first iterated to a stable state, corresponding to a fitness value of 0.612. In the comparison of AUC values, the ROC area under the curve of the research method, Blockchain technology, X-IDEA, and image encryption algorithms based on hyperchaotic systems and improved quantum rotation gates were 0.978, 0.967, 0.951, and 0.914, respectively. The values of the research method were significantly larger. In the comparison of average classification accuracy, on dataset A, when the system iterated 75 times, the research method had the maximum classification accuracy, with a value of 98.24%. The stable average classification accuracy of Blockchain technology, X-IDEA algorithm, and image encryption algorithms based on hyper chaotic systems and improved quantum rotation gates were 93.21%, 94.57%, and 96.23%, respectively. Compared to the HS-IQRG technology, the encryption accuracy of the research method was 0.963, 0.977, 0.968, 0.979, and 0.958 when the PU power was 0.5:1, 1.0:1, 1.5:1, 2.0:1, and 2.5:1, respectively. When the system ran on dataset A 44 times, the time it took for the research method to reach a stable state was only 0.0424 seconds, which was 0.0008 seconds faster than the HS-IQRG technology. The above results all show that the research method can encrypt data. Meanwhile, this method learns a safe password generation method in the automated system, which makes certain contributions to computer communications. This experiment provides a new theoretical perspective on achieving more secure computer communication systems by combining CCA and ANC technologies. From a technical point of view, the effectiveness of the proposed method is verified through performance testing, which provides an experimental basis for the application of the technology. Meanwhile, the application potential of this technology in different network environments is discussed, providing a valuable reference for future communication security practices.

•There is a growing need to identify benchmark parameters in advancing a low dimensional representation of functional connectivity (i.e., gradients) into a reliable biomarker.•Here, we explored multidimensional parameter space in calculating functional gradients to improve their reproducibility, reliability and predictive validity.•We demonstrated that more reproducible and reliable gradient markers tend to have higher predictive power for unseen phenotypic scores across various cognitive domains.•We showed that the low-dimensional connectivity gradient approach could outperform conventional edge-based analyses in terms of predicting phenotypic scores.•We highlight the necessity of optimizing parameters for new imaging methods before their widespread deployment. Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent efforts to address this challenge have capitalized on dimensionality reduction techniques applied to resting-state fMRI, identifying principal components of intrinsic connectivity which describe smooth transitions across different cortical systems, so called “connectivity gradients”. These gradients recapitulate neurocognitively meaningful organizational principles that are present in both human and primate brains, and also appear to differ among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209 × 2) and the Midnight scan club (n = 9), we tested the following key biomarker traits – reliability, reproducibility and predictive validity – of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (resting-state fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95–97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional and multivariate gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.