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Abstract Glioblastoma (GBM) is a highly aggressive, infiltrative malignancy that cannot be completely cured by current treatment modalities, and therefore requires more precise molecular subtype signatures to predict treatment response for personalized precision therapy. Expression subtypes of GBM samples from the Cancer Genome Atlas (TCGA) were identified using BayesNM and compared with existing molecular subtypes of GBM. Biological features of the subtypes were determined by single-sample gene set enrichment analysis. Genomic and proteomic data from GBM samples were combined and Genomic Identification of Significant Targets in Cancer analysis was used to screen genes with recurrent somatic copy-number alterations phenomenon. The immune environment among subtypes was compared by assessing the expression of immune molecules and the infiltration of immune cells. Molecular subtypes adapted to immunotherapy were identified based on Tumor Immune Dysfunction and Exclusion (TIDE) score. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was performed on the expression profiles of S2, S3 and S4 in TCGA-GBM and RPPA to determine the respective corresponding best predictive model. Four novel molecular subtypes were classified. Specifically, S1 exhibited a low proliferative profile; S2 exhibited the profile of high proliferation, IDH1 mutation, TP53 mutation and deletion; S3 was characterized by high immune scores, innate immunity and adaptive immune infiltration scores, with the lowest TIDE score and was most likely to benefit from immunotherapy; S4 was characterized by high proliferation, EGFR amplification, and high protein abundance, and was the most suitable subtype for bevacizumab. LASSO analysis constructed the best prediction model composed of 13 genes in S2 with an accuracy of 96.7%, and the prediction model consisting of 17 genes in S3 with an accuracy of 86.7%, and screened 14 genes as components of the best prediction model in S4 with an accuracy of 93%. To conclude, our study classified reproducible and robust molecular subtypes of GBM, and these findings might contribute to the identification of patients responding to immunotherapy, thereby improving GBM prognosis.

Abstract Objective To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD). Materials and methods The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic. Results Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p

Abstract Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

Background Currently, no universally accepted standardized VTE risk assessment model (RAM) is specifically designed for critically ill patients. Although the ICU-venous thromboembolism (ICU-VTE) RAM was initially developed in 2020, it lacks prospective external validation. Objectives To evaluate the predictive performance of the ICU-VTE RAM in terms of VTE occurrence in mixed medical-surgical ICU patients. Methods We prospectively enrolled adult patients in the ICU. The ICU-VTE score and Caprini or Padua score were calculated at admission, and the incidence of in-hospital VTE was investigated. The performance of the ICU-VTE RAM was evaluated and compared with that of Caprini or Padua RAM using the receiver operating curve. Results We included 269 patients (median age: 70 years; 62.5% male). Eighty-three (30.9%) patients experienced inpatient VTE. The AUC of the ICU-VTE RAM was 0.743 (95% CI, 0.682-0.804, P

Abstract Background Breast cancer has the highest incidence rate of cancer worldwide, and brain metastases (BrM) are among the most malignant cases. While some patients have benefited from immune checkpoint inhibitors (ICIs), the complex anatomical structure of the brain and the heterogeneity of metastatic tumors have made it difficult to characterize the tumor immune microenvironment (TME) of metastatic tumors. Methods To address this, we used single‐cell RNA sequencing (scRNA‐seq) to analyze immune cells in the cerebrospinal fluid (CSF) of BrM patients with breast cancer, thereby providing a comprehensive view of the immune microenvironment landscape of BrM. Results Based on canonical marker genes, we identified nine cell types, and further identified their subtypes through differential expression gene (DEG) analysis. We compared the changes in cells and functions in the immune microenvironment of patients with different prognoses. Our analysis revealed a series of genes that promote tumor immune function (CCR5, LYZ, IGKC, MS4A1, etc.) and inhibit tumor immune function (SCGB2A2, CD24, etc.). Conclusions The scRNA‐seq in CSF provides a noninvasive method to describe the TME of breast cancer patients and guide immunotherapy.

BackgroundPrevious neuroimaging studies have revealed structural and functional brain abnormalities in patients with cervical spondylosis (CS). However, the results are divergent and inconsistent. Therefore, the present study conducted a multi-modal meta-analysis to investigate the consistent structural and functional brain alterations in CS patients.MethodsA comprehensive literature search was conducted in five databases to retrieve relevant resting-state functional magnetic resonance imaging (rs-fMRI), structural MRI and diffusion tensor imaging (DTI) studies that measured brain functional and structural differences between CS patients and healthy controls (HCs). Separate and multimodal meta-analyses were implemented, respectively, by employing Anisotropic Effect-size Signed Differential Mapping software.Results13 rs-fMRI studies that used regional homogeneity, amplitude of low-frequency fluctuations (ALFF) and fractional ALFF, seven voxel-based morphometry (VBM) studies and one DTI study were finally included in the present research. However, no studies on surface-based morphometry (SBM) analysis were included in this research. Due to the insufficient number of SBM and DTI studies, only rs-fMRI and VBM meta-analyses were conducted. The results of rs-fMRI meta-analysis showed that compared to HCs, CS patients demonstrated decreased regional spontaneous brain activities in the right lingual gyrus, right middle temporal gyrus (MTG), left inferior parietal gyrus and right postcentral gyrus (PoCG), while increased activities in the right medial superior frontal gyrus, bilateral middle frontal gyrus and right precuneus. VBM meta-analysis detected increased GMV in the right superior temporal gyrus (STG) and right paracentral lobule (PCL), while decreased GMV in the left supplementary motor area and left MTG in CS patients. The multi-modal meta-analysis revealed increased GMV together with decreased regional spontaneous brain activity in the left PoCG, right STG and PCL among CS patients.ConclusionThis meta-analysis revealed that compared to HCs, CS patients had significant alterations in GMV and regional spontaneous brain activity. The altered brain regions mainly included the primary visual cortex, the default mode network and the sensorimotor area, which may be associated with CS patients' symptoms of sensory deficits, blurred vision, cognitive impairment and motor dysfunction. The findings may contribute to understanding the underlying pathophysiology of brain dysfunction and provide references for early diagnosis and treatment of CS.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, CRD42022370967.

Abstract Background The gastrointestinal tract contains a massive microbiota, and targeting the gut could be a potential intervention for sepsis. However, the interaction between sepsis and the intestinal microbiota is defined as an “incompletely understood bidirectional relationship”. Methods This retrospective observational cohort study investigated the fecal microbiota of sepsis patients admitted to the Department of Critical Care Medicine of the Central Hospital of Wuhan, China, from May 2019 to January 2020. 14 septic patients were divided into the non-severe group and the severe group according to the Acute Physiology and Chronic Health Evaluation II (APACHE II) score. Herein, fecal samples were serially collected on admission, the third, fourth, and fifth days, and ICU discharge. The fecal microbiota was analyzed by 16S rRNA gene sequencing and its correlation with clinical parameters was evaluated. Results Bacteroidetes, Firmicutes, and Proteobacteria were dominant phyla at ICU admission, and fecal biodiversity was not significantly different between the non-severe group (APACHE II 15). However, the diversity of the gut microbiota was significantly lower at ICU discharge than that at ICU admission with the extension of treatment time. Further significant difference flora analysis (LEfSe) showed that the genera Veillonella and Ruminococcus were the most discriminant biomarkers at ICU admission in non-severe and severe patients, respectively, while Enterococcus was the most discriminant biomarker at ICU discharge in all septic patients. Of note, liver function tests, including ALT, AST, TBIL, and DBIL correlated with the prevalence of various bacterial genera. Conclusions The diversity of the gut microbiota in patients with sepsis decreases dramatically during ICU stay, and there are distinct dynamic changes in gut microbiota among patients with different severity in sepsis.

Abstract Purpose To predict ductal carcinoma in situ with microinvasion (DCISMI) based on clinicopathologic, conventional breast magnetic resonance imaging (MRI), and dynamic contrast enhanced MRI (DCE-MRI) radiomics signatures in women with biopsy-confirmed ductal carcinoma in situ (DCIS). Methods Eighty-six women with eighty-seven biopsy-proven DCIS who underwent preoperative MRI and underwent surgery were retrospectively identified. Clinicopathologic, conventional MRI, DCE-MRI radiomics, combine (based on conventional MRI and DCE-MRI radiomics), traditional (based on clinicopathologic and conventional MRI) and mixed (based on clinicopathologic, conventional MRI and DCE-MRI radiomics) models were constructed by logistic regression (LR) with a 3-fold cross-validation, all evaluated using receiver operating characteristic (ROC) curve analysis. A clinical radiomics nomogram was then built by incorporating the Radiomics score, significant clinicopathologic and conventional MRI features of mixed model. Results The area under the curves (AUCs) of clinicopathologic, conventional MRI, DCE-MRI radiomics, traditional, combine, and mixed model were 0.76 (95% confidence interval [CI] 0.59–0.94), 0.77 (95%CI 0.59–0.95), 0.74 (95%CI 0.55–0.93), 0.87 (95%CI 0.73–1), 0.8 (95%CI 0.63–0.96), and 0.93 (95%CI 0.84–1) in the validation cohort, respectively. The clinical radiomics nomogram based on mixed model showed higher AUCs than both clinicopathologic and DCE-MRI radiomics models in training/test (all P

Abstract Background Differentiating between low-grade glioma and brain glial cell hyperplasia is crucial for the customized clinical treatment of patients. Objective Based on multiparametric MRI imaging and clinical risk factors, a radiomics-clinical model and nomogram were constructed for the distinction of brain glial cell hyperplasia from low-grade glioma. Methods Patients with brain glial cell hyperplasia and low-grade glioma who underwent surgery at the First Affiliated Hospital of Soochow University from March 2016 to March 2022 were retrospectively included. In this study, A total of 41 patients of brain glial cell hyperplasia and 87 patients of low-grade glioma were divided into training group and validation group randomly at a ratio of 7:3. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1-enhanced). Then, LASSO, SVM, and RF models were created in order to choose a model with a greater level of efficiency for calculating each patient’s Rad-score (radiomics score). The independent risk factors were identified via univariate and multivariate logistic regression analysis to filter the Rad-score and clinical risk variables in turn. A radiomics-clinical model was next built of which effectiveness was assessed. Results Brain glial cell hyperplasia and low-grade gliomas from the 128 cases were randomly divided into 10 groups, of which 7 served as training group and 3 as validation group. The mass effect and Rad-score were two independent risk variables used in the construction of the radiomics-clinical model, and their respective AUCs for the training group and validation group were 0.847 and 0.858. The diagnostic accuracy, sensitivity, and specificity of the validation group were 0.821, 0.750, and 0.852 respectively. Conclusion Combining with radiomics constructed by multiparametric MRI images and clinical features, the radiomics-clinical model and nomogram that were developed to distinguish between brain glial cell hyperplasia and low-grade glioma had a good performance.

Abstract Background Type 2 diabetes mellitus (T2DM) patients have a higher incidence of coronary artery disease than the general population. The aim of this study was to develop a radiomics nomogram of pericoronary adipose tissue (PCAT) based on non-contrast CT to predict haemodynamically significant coronary stenosis in T2DM patients. Methods The study enrolled 215 T2DM patients who underwent non-contrast CT and coronary computed tomography angiography (CCTA). CCTA derived fractional flow reserve (FFRCT) ≤ 0.80 was defined as hemodynamically significant stenosis.1691 radiomics features were extracted from PCAT on non-contrast CT. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to select useful radiomics features to construct Radscore. Logistic regression was applied to select significant factors among Radscore, fat attenuation index (FAI) and coronary artery calcium score (CACS) to construct radiomics nomogram. Results Radscore [odds ratio (OR) = 2.84; P

BackgroundIrritable bowel syndrome (IBS) is a brain-gut disorder with high global prevalence, resulting from abnormalities in brain connectivity of the default mode network and aberrant changes in gray matter (GM). However, the findings of previous studies about IBS were divergent. Therefore, we conducted a meta-analysis to identify common functional and structural alterations in IBS patients.MethodsAltogether, we identified 12 studies involving 194 IBS patients and 230 healthy controls (HCs) from six databases using whole-brain resting state functional connectivity (rs-FC) and voxel-based morphometry. Anisotropic effect-size signed differential mapping (AES-SDM) was used to identify abnormal functional and structural changes as well as the overlap brain regions between dysconnectivity and GM alterations.ResultsFindings indicated that, compared with HCs, IBS patients showed abnormal rs-FC in left inferior parietal gyrus, left lingual gyrus, right angular gyrus, right precuneus, right amygdala, right median cingulate cortex, and left hippocampus. Altered GM was detected in the fusiform gyrus, left triangular inferior frontal gyrus (IFG), right superior marginal gyrus, left anterior cingulate gyrus, left rectus, left orbital IFG, right triangular IFG, right putamen, left superior parietal gyrus and right precuneus. Besides, multimodal meta-analysis identified left middle frontal gyrus, left orbital IFG, and right putamen as the overlapped regions.ConclusionOur results confirm that IBS patients have abnormal alterations in rs-FC and GM, and reveal brain regions with both functional and structural alterations. These results may contribute to understanding the underlying pathophysiology of IBS.Systematic review registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42022351342.

Social networks have become one of the most popular platforms for people to communicate and interact with their friends and share personal information and experiences (e.g., Facebook owns over 1.23 billion monthly active users). The increasing popularity of social networks has generated extremely large-scale user data (e.g., Twitter generates 500 million tweets per day and around 200 billion tweets per year). These data can help improve people’s quality of life as well as benefit various interest groups such as advertisers, application developers, and so on. However, privacy may be compromised if learning algorithms are used to infer unpublished privacy information from published data. Hence, user data privacy preservation has become one of the most urgent research issues in social networks.

Migraine—a disabling neurological disorder, imposes a tremendous burden on societies. To reduce the economic and health toll of the disease, insight into its pathophysiological mechanism is key to improving treatment and prevention. Resting-state functional magnetic resonance imaging (rs-fMRI) studies suggest abnormal functional connectivity (FC) within the default mode network (DMN) in migraine patients. This implies that DMN connectivity change may represent a biomarker for migraine. However, the FC abnormalities appear inconsistent which hinders our understanding of the potential neuropathology. Therefore, we performed a meta-analysis of the FC within the DMN in migraine patients in the resting state to identify the common FC abnormalities. With efficient search and selection strategies, nine studies (published before July, 2022) were retrieved, containing 204 migraine patients and 199 healthy subjects. We meta-analyzed the data using the Anisotropic Effect Size version of Signed Differential Mapping (AES-SDM) method. Compared with healthy subjects, migraine patients showed increased connectivity in the right calcarine gyrus, left inferior occipital gyrus, left postcentral gyrus, right cerebellum, right parahippocampal gyrus, and right posterior cingulate gyrus, while decreased connectivity in the right postcentral gyrus, left superior frontal gyrus, right superior occipital gyrus, right orbital inferior frontal gyrus, left middle occipital gyrus, left middle frontal gyrus and left inferior frontal gyrus. These results provide a new perspective for the study of the pathophysiology of migraine and facilitate a more targeted treatment of migraine in the future.

ObjectivesThis study aimed to determine whether texture analysis (TA) and machine learning-based classifications can be applied in differential diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) using non-contrast cine cardiac magnetic resonance (CMR) images.MethodsIn this institutional review board-approved study, we consecutively enrolled 167 patients with CA (n = 85), HCM (n = 82), and 84 patients with normal CMR served as controls. All cases were randomized into training [119 patients (70%)] and validation [48 patients (30%)] groups. A total of 275 texture features were extracted from cine images. Based on regression analysis with the least absolute shrinkage and selection operator (LASSO), nine machine learning models were established and their diagnostic performance determined.ResultsNineteen radiomics texture features derived from cine images were used to differentiate CA and HCM. In the validation cohort, the support vector machine (SVM), which had an accuracy of 0.85, showed the best performance (MCC = 0.637). Gray level non-uniformity (GLevNonU) was the single most effective feature. The combined model of radiomics texture features and conventional MR metrics had superior discriminatory performance (AUC = 0.89) over conventional MR metrics model (AUC = 0.79). Moreover, results showed that GLevNonU levels in HCM patients were significantly higher compared with levels in CA patients and control groups (P < 0.001). A cut-off of GLevNonU ≥ 25 was shown to differentiate between CA and HCM patients, with an area under the curve (AUC) of 0.86 (CI:0.804–0.920). Multiple comparisons tests showed that GLevNonU was significantly greater in LGE+, relative to LGE-patient groups (CA+ vs. CA- and HCM+ vs. HCM-, P = 0.01, 0.001, respectively).ConclusionMachine learning-based classifiers can accurately differentiate between CA and HCM on non-contrast cine images. The radiomics-MR combined model can be used to improve the discriminatory performance. TA may be used to assess myocardial microstructure changes that occur during different stages of cardiomyopathies.

One of the most significant defects, porosity, has been proven to affect the properties of composites. It is critical to reduce the porosity of composite material and the curing cost while maintaining high laminate quality for vacuum bag-only prepreg. In this paper, a rapidly cured epoxy resin system was developed, and an alkali-free glass fiber fabric prepreg suitable for vacuum bag molding was prepared by the asymmetric impregnation method. The optimal curing process for the prepreg was determined by resin curing kinetics, dielectric viscosity, initial curing temperature, and curing time of the prepreg on the laminate quality. The optimal curing profile of the prepreg was obtained. In addition, the effect of room temperature exposure time on the properties of the prepreg was also evaluated. These laminates produced by vacuum bag molding had outstanding internal quality and mechanical properties via the changes in the asymmetric impregnation process and the curing procedure.

Abstract Deep learning (DL) has been applied to digital signal modulation identification (DSMI) due to its powerful feature learning ability. However, most of the existing DL‐based DSMI methods are limited to specific experimental scene relating to the additive white Gaussian noise (AWGN) channel or static multipath channel. The result is that the trained network has deteriorative identification accuracy when the channel conditions change unless retrained. To solve the problem, this paper proposes a DSMI method suitable for orthogonal frequency division multiplexing (OFDM) under different multipath channels, including the variation of delay, path number and channel coefficient. This method can accurately detect the modulation feature rather than the channel's to identify the modulation type, thus reducing the network training amount. The method is divided into two parts. Firstly, traditional signal processing methods are combined, including various channel estimators and equalisers to compensate for the channel. Then a robust DL network, RSN‐MI, is designed as a classifier. Unlike other DL‐based DSMI methods, the influence of signal processing algorithms on DSMI performance are focused on rather than model parameters. Besides, the proposed classifier is compared with the DSMI classifier in other contributions. The results show that the classifier works better in different multipath channels.

ObjectiveThe aim of this study was to investigate the spontaneous regional neural activity abnormalities in patients with acute basal ganglia ischemic stroke (BGIS) using a multifrequency bands regional homogeneity (ReHo) method and to explore whether the alteration of ReHo values was associated with clinical characteristics.MethodsIn this study, 34 patients with acute BGIS and 44 healthy controls (HCs) were recruited. All participants were examined by resting-state functional magnetic resonance imaging (rs-fMRI). The ReHo method was used to detect the alterations of spontaneous neural activities in patients with acute BGIS. A two-sample t-test comparison was performed to compare the ReHo value between the two groups, and a Pearson correlation analysis was conducted to assess the relationship between the regional neural activity abnormalities and clinical characteristics.ResultsCompared with the HCs, the patients with acute BGIS showed increased ReHo in the left caudate and subregions such as the right caudate and left putamen in conventional frequency bands. In the slow-5 frequency band, patients with BGIS showed decreased ReHo in the left medial cingulum of BGIS compared to the HCs and other subregions such as bilateral caudate and left putamen. No brain regions with ReHo alterations were found in the slow-4 frequency band. Moreover, we found that the ReHo value of left caudate was positively correlated with the NIHSS score.ConclusionOur findings revealed the alterations of ReHo in patients with acute BGIS in a specific frequency band and provided a new insight into the pathogenesis mechanism of BGIS. This study demonstrated the frequency-specific characteristics of ReHo in patients with acute BGIS, which may have a positive effect on the future neuroimaging studies.

BackgroundPredicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer.ObjectiveTo explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery.MethodsPatients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated.ResultsOf the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively.ConclusionCombining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance.

ObjectiveThis study aimed to establish a model embraced electromechanical coupling time (EMC-T) and assess the value of the model for the prediction of heart failure (HF) in patients with hypertrophic cardiomyopathy (HCM).Materials and MethodsData on 82 patients with HCM at Shaanxi Provincial People’s Hospital between February 2019 and November 2021 were collected and then formed the training dataset (n = 82). Data were used to screen predictors of HF using univariate and multivariate analyses. Predictors were implemented to discover the optimal cut-off value, were incorporated into a model, and shown as a nomogram. The cumulative HF curve was calculated using the Kaplan–Meier method. Additionally, patients with HCM at other hospitals collected from March 2019 to March 2021 formed the validation dataset. The model’s performance was confirmed both in training and validation sets.ResultsDuring a median of 22.91 months, 19 (13.38%) patients experienced HF. Cox analysis showed that EMC-T courses in the lateral wall, myoglobin, PR interval, and left atrial volume index were independent predictors of HF in patients with HCM. Five factors were incorporated into the model and shown as a nomogram. Stratification of patients into two risk subgroups by applying risk score (

ObjectivesTo develop and validate a nomogram model based on radiomics features for preoperative prediction of visceral pleural invasion (VPI) in patients with lung adenocarcinoma.MethodsA total of 659 patients with surgically pathologically confirmed lung adenocarcinoma underwent CT examination. All cases were divided into a training cohort (n = 466) and a validation cohort (n = 193). CT features were analyzed by two chest radiologists. CT radiomics features were extracted from CT images. LASSO regression analysis was applied to determine the most useful radiomics features and construct radiomics score (radscore). A nomogram model was developed by combining the optimal clinical and CT features and the radscore. The model performance was evaluated using ROC analysis, calibration curve and decision curve analysis (DCA).ResultsA total of 1316 radiomics features were extracted. A radiomics signature model with a selection of the six optimal features was developed to identify patients with or without VPI. There was a significant difference in the radscore between the two groups of patients. Five clinical features were retained and contributed as clinical feature models. The nomogram combining clinical features and radiomics features showed improved accuracy, specificity, positive predictive value, and AUC for predicting VPI, compared to the radiomics model alone (specificity: training cohort: 0.89, validation cohort: 0.88, accuracy: training cohort: 0.84, validation cohort: 0.83, AUC: training cohort: 0.89, validation cohort: 0.89). The calibration curve and decision curve analyses suggested that the nomogram with clinical features is beyond the traditional clinical and radiomics features.ConclusionA nomogram model combining radiomics and clinical features is effective in non-invasively prediction of VPI in patients with lung adenocarcinoma.

Abstract Background To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. Methods The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. Results In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5–9) was significantly higher than that of the mild group (4, IQR,2–5) (P

Abstract Background There is a lack of well-established clinical tools for predicting dendritic cell (DC) vaccination response of pancreatic ductal adenocarcinoma (PDAC). DC vaccine treatment efficiency was demonstrated using histological analysis in pre-clinical studies; however, its usage was limited due to invasiveness. In this study, we aimed to investigate the potential of MRI texture features for detection of early immunotherapeutic response as well as overall survival (OS) of PDAC subjects following dendritic cell (DC) vaccine treatment in LSL-Kras G12D ;LSL-Trp53 R172H ;Pdx-1-Cre (KPC) transgenic mouse model of pancreatic ductal adenocarcinoma (PDAC). Materials and methods KPC mice were treated with DC vaccines, and tumor growth was dynamically monitored. A total of a hundred and fifty-two image features of T2-weighted MRI images were analyzed using a kernel-based support vector machine model to detect treatment effects following the first and third weeks of the treatment. Moreover, univariate analysis was performed to describe the association between MRI texture and survival of KPC mice as well as histological tumor biomarkers. Results OS for mice in the treatment group was 54.8 ± 22.54 days while the control group had 35.39 ± 17.17 days. A subset of three MRI features distinguished treatment effects starting from the first week with increasing accuracy throughout the treatment (75% to 94%). Besides, we observed that short-run emphasis of approximate wavelet coefficients had a positive correlation with the survival of the KPC mice (r = 0.78, p

Objective. The purpose of this study was to investigate the characteristics of different frequency bands in the spontaneous brain activity among patients with acute basal ganglia ischemic stroke (BGIS). Methods. In the present study, thirty-four patients with acute BGIS and forty-four healthy controls were examined by resting-state functional magnetic resonance imaging (rs-fMRI) from May 2019 to December 2020. Two amplitude methods including amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) calculated in three frequency bands (conventional frequency band: 0.01-0.08 Hz; slow-5 frequency band: 0.01-0.027 Hz; and slow-4 frequency band: 0.027-0.073 Hz) were conducted to evaluate the spontaneous brain activity in patients with acute BGIS and healthy controls (HCs). Gaussian Random Field Theory (GRF, voxel p

Background. Transient ischemic attack (TIA) is a known risk factor for stroke. Abnormal alterations in the low-frequency range of the gray matter (GM) of the brain have been studied in patients with TIA. However, whether there are abnormal neural activities in the low-frequency range of the white matter (WM) in patients with TIA remains unknown. The current study applied two resting-state metrics to explore functional abnormalities in the low-frequency range of WM in patients with TIA. Furthermore, a reinforcement learning method was used to investigate whether altered WM function could be a diagnostic indicator of TIA. Methods. We enrolled 48 patients with TIA and 41 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) and clinical/physiological/biochemical data were collected from each participant. We compared the group differences between patients with TIA and HCs in the low-frequency range of WM using two resting-state metrics: amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). The altered ALFF and fALFF values were defined as features of the reinforcement learning method involving a Q-learning algorithm. Results. Compared with HCs, patients with TIA showed decreased ALFF in the right cingulate gyrus/right superior longitudinal fasciculus/left superior corona radiata and decreased fALFF in the right cerebral peduncle/right cingulate gyrus/middle cerebellar peduncle. Based on these two rs-fMRI metrics, an optimal Q-learning model was obtained with an accuracy of 82.02%, sensitivity of 85.42%, specificity of 78.05%, precision of 82.00%, and area under the curve (AUC) of 0.87. Conclusion. The present study revealed abnormal WM functional alterations in the low-frequency range in patients with TIA. These results support the role of WM functional neural activity as a potential neuromarker in classifying patients with TIA and offer novel insights into the underlying mechanisms in patients with TIA from the perspective of WM function.

Background: Lung adenocarcinoma (LUAD) is an exceedingly diverse disease, making prognostication difficult. Inflammatory responses in the tumor or the tumor microenvironment can alter prognosis in the process of the ongoing cross-talk between the host and the tumor. Nonetheless, Inflammatory response-related genes’ prognostic significance in LUAD, on the other hand, has yet to be determined.Materials and Methods: The clinical data as well as the mRNA expression patterns of LUAD patients were obtained from a public dataset for this investigation. In the TCGA group, a multigene prognostic signature was built utilizing LASSO Cox analysis. Validation was executed on LUAD patients from the GEO cohort. The overall survival (OS) of low- and high-risk cohorts was compared utilizing the Kaplan-Meier analysis. The assessment of independent predictors of OS was carried out utilizing multivariate and univariate Cox analyses. The immune-associated pathway activity and immune cell infiltration score were computed utilizing single-sample gene set enrichment analysis. GO keywords and KEGG pathways were explored utilizing gene set enrichment analysis.Results: LASSO Cox regression analysis was employed to create an inflammatory response-related gene signature model. The high-risk cohort patients exhibited a considerably shorter OS as opposed to those in the low-risk cohort. The prognostic gene signature’s predictive ability was demonstrated using receiver operating characteristic curve analysis. The risk score was found to be an independent predictor of OS using multivariate Cox analysis. The functional analysis illustrated that the immune status and cancer-related pathways for the two-risk cohorts were clearly different. The tumor stage and kind of immune infiltrate were found to be substantially linked with the risk score. Furthermore, the cancer cells’ susceptibility to anti-tumor medication was substantially associated with the prognostic genes expression levels.Conclusion: In LUAD, a new signature made up of 8 inflammatory response-related genes may be utilized to forecast prognosis and influence immunological state. Inhibition of these genes could also be used as a treatment option.

In recent years, the various Internet of Things (IoT) communication technologies have been researched. The third generation partnership project (3GPP) is dedicated to building the IoT platforms, including NB-IoT, eMTC, and URLLC. However, these platforms cannot adequately meet all the needs of future IoT use cases which are usually broad and beyond cellular. In order to reduce the deployment cost, making the full use of the unlicensed spectrum is strongly appealing to the IoT scenarios. Transform domain communication system (TDCS) is a typical system with spectrum sensing and sharing, and it has unique advantages in supporting multiuser communication in a complex electromagnetic environment. Thus, TDCS is proposed for the IoT massive multiple access scenarios. Previous studies indicate that traditional TDCS has poor BER performance when encountering a large number of users, especially with the near-far effect. To address this, a hybrid clustering TDCS targeting at cognitive IoT applications is proposed to support massive multiple access with the near-far effect. The optimal construction method of the sequence set is also given for the hybrid clustering TDCS. Compared with traditional TDCS, the simulation results show that the proposed system achieves an improvement in multiple access ability, and the system performance can be improved by making a compromise on the number of clusters.

Mobile edge computing (MEC) is pervasive to support high-speed end-to-end data transmission and computation for 5th generation (5G) mobile communications by offloading computation tasks to the cloud at the edge of a cellular base station. In a cellular network for healthcare applications, we must consider the different priorities of data transmission and computation by referring to the emergency levels of patients. A critical issue of determining the allocation of wireless resources among users is the authenticity of their priorities (i.e., emergency levels). Without authenticating the users' priorities, a user who advocates a higher emergency level can allocate more wireless resources, leading to the insufficient wireless resources to the users who are actually in high priorities. In this paper, we investigate the optimization of end-to-end delay of wireless users in a cellular network with the authenticity of their priorities by blockchain consensus and propose an algorithm of allocating communication and computation resources to minimize the delay of data transmission and computation. The numerical results illustrate that our proposed algorithm is capable of dramatically reducing the end-to-end delay under the various scenarios of healthcare applications.

In this paper, we present a novel sequence design for efficient channel estimation in multiple input multiple output filterbank multicarrier (MIMO-FBMC) system with offset QAM modulation. Our proposed sequences, transmitted over one FBMC/OQAM symbol, are real-valued in the frequency domain and display zero-correlation zone properties in the time-domain. The latter property enables optimal channel estimation for a least-square estimator in frequency-selective fading channels. To further improve the system performance, we mitigate the data interference by an iterative feedback loop between channel estimation and FBMC demodulation. The simulation results validate that our proposed real-valued orthogonal sequences and the iterative channel estimation and demodulation scheme provide a practical solution for enhanced performance in preamble-based MIMO-FBMC systems.

ObjectiveTo determine whether machine learning based on conventional magnetic resonance imaging (MRI) sequences have the potential for the differential diagnosis of multiple myeloma (MM), and different tumor metastasis lesions of the lumbar vertebra.MethodsWe retrospectively enrolled 107 patients newly diagnosed with MM and different metastasis of the lumbar vertebra. In total 60 MM lesions and 118 metastasis lesions were selected for training classifiers (70%) and subsequent validation (30%). Following segmentation, 282 texture features were extracted from both T1WI and T2WI images. Following regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm, the following machine learning models were selected: Support‐Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Artificial Neural Networks (ANN), and Naïve Bayes (NB) using 10-fold cross validation, and the performances were evaluated using a confusion matrix. Matthews correlation coefficient (MCC), sensitivity, specificity, and accuracy of the models were also calculated.ResultsTo differentiate MM and metastasis, 13 features in the T1WI images and 9 features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.605) with accuracy, sensitivity, and specificity of 0.815, 0.879, and 0.790, respectively, in the validation cohort. To differentiate MM and metastasis subtypes, eight features in the T1WI images and seven features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.560, 0.412, 0.449), respectively, with accuracy = 0.648; sensitivity 0.714, 0.821, 0.897 and specificity 0.775, 0.600, 0.640 for the MM, lung, and other metastases, respectively, in the validation cohort.ConclusionsMachine learning–based classifiers showed a satisfactory performance in differentiating MM lesions from those of tumor metastasis. While their value for distinguishing myeloma from different metastasis subtypes was moderate.

Individuals with subjective cognitive decline (SCD) are more likely to develop into Alzheimer disease (AD) in the future. Resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown alterations of intrinsic brain activity (IBA) in SCD individuals. However, rs-fMRI studies to date have mainly focused on static characteristics of IBA, with few studies reporting dynamics- and concordance-related changes in IBA indices in SCD individuals. To investigate these aberrant changes, a temporal dynamic analysis of rs-fMRI data was conducted on 94 SCD individuals (71.07 ± 6.18 years, 60 female), 75 (74.36 ± 8.42 years, 35 female) mild cognitive impairment (MCI) patients, and 82 age-, gender-, and education-matched controls (NCs; 73.88 ± 7.40 years, 49 female) from the Alzheimer's Disease Neuroimaging Initiative database. The dynamics and concordance of the rs-fMRI indices were calculated. The results showed that SCD individuals had a lower amplitude of low-frequency fluctuations dynamics in bilateral hippocampus (HP)/parahippocampal gyrus (PHG)/fusiform gyrus (FG) and bilateral cerebellum, a lower fractional amplitude of low-frequency fluctuation dynamics in bilateral precuneus (PreCu) and paracentral lobule, and a lower regional homogeneity dynamics in bilateral cerebellum, vermis, and left FG compared with the other two groups, whereas those in MCI patients were higher (Gaussian random field–corrected, voxel-level P < 0.001, cluster-level P < 0.05). Furthermore, SCD individuals had higher concordance in bilateral HP/PHG/FG, temporal lobe, and left midcingulate cortex than NCs, but those in MCI were lower than those in NCs. No correlation between concordance values and neuropsychological scale scores was found. SCD individuals showed both dynamics and concordance-related alterations in IBA, which indicates a compensatory mechanism in SCD individuals. Temporal dynamics analysis offers a novel approach to capturing brain alterations in individuals with SCD.

Background: Hypothesis-driven functional connectivity (FC) analyses have revealed abnormal functional interaction of regions or networks involved in pain processing in episodic migraine patients. We aimed to investigate the resting-state FC patterns in episodic migraine by combining data-driven voxel-wise degree centrality (DC) calculation and seed-based FC analysis.Methods: Thirty-nine patients suffering from episodic migraine without aura and 35 healthy controls underwent clinical assessment and functional MRI. DC was analyzed voxel-wise and compared between groups, and FC of regions with DC differences were further examined using a seed-based approach.Results: Compared with the control group, the migraine group showed increased and decreased DC in the right posterior insula and left crus I, respectively. Seed-based FC analyses revealed that migraine patients demonstrated increased right posterior insula connections with the postcentral gyrus, supplementary motor area/paracentral lobule, fusiform gyrus and temporal pole. The left crus I showed decreased FC with regions of the default mode network (DMN), including the medial prefrontal cortex (mPFC), angular gyrus, medial and lateral temporal cortex in patients with migraine. Furthermore, pain intensity positively correlated with DC in the right amygdala/parahippocampal gyrus, and migraine frequency negatively correlated with FC between the left crus I and mPFC.Conclusion: Patients with episodic migraine without aura have increased FC with the right posterior insula and decreased FC within the DMN, which may underlie disturbed sensory integration and cognitive processing of pain. The left crus I-mPFC connectivity may be a useful biomarker for assessing migraine frequency.

Abstract A joint radar and wireless communication (RadCom) fusion system is an important trend for future intelligent transportation, such as the Internet of Vehicles (IOV). However, traditional joint orthogonal frequency division multiplexing (OFDM)-based RadCom fusion system suffers from the problem of high peak-to-average power ratio (PAPR) of transmission signals, leading to nonlinear distortion in high power amplifiers (HPAs). To solve this problem, a new constant envelope OFDM RadCom approach is proposed in this paper. Taking phase modulation into consideration, the constant envelope OFDM RadCom system obtains 0 dB PAPR value, which results in linear high power amplifier being feasible. In the receiver side, both communication transmission and target detection are guaranteed by a phase demodulator and frequency-domain signal processing algorithm, respectively. Simulation results demonstrate that the constant envelope OFDM RadCom system is a feasible solution for future IOV with the capability of target detection and wireless communication simultaneously.

Evolving from cognitive radio networks (CRNs), the concept has developed into a new paradigm of cloud and big data-based next-generation CRNs due to huge amount of data processing, complicated spectrum resource scheduling, and real-time information exchange. In cloud-based CRNs, control channels are needed for CR nodes to perform certain handshakings for network self-organization, spectrum sensing, network coordination, and flexible data connections. This paper investigates a transmission scheme for control channel (CC) in cloud-based CRNs, which is over several noncontiguous spectral holes. Transform domain communication system (TDCS)-based transmission scheme with spectrally-constrained sequence design is presented for CC. A practical testbed design for TDCS-based CC with multiple National Instruments PXIe devices and six universal software defined radio reconfigurable input/output devices is presented. Details of system design as well as main implementation challenges are described. Bit-error rate of the system is validated through both theoretical analysis and simulation results under realistic channel conditions.

The increasing demand in 5G wireless systems has brought significant improvement in transmission speed with lower latency, especially in ultra-dense networks. The demand of privacy and security are becoming more critical in such large scale wireless networks. Different from traditional encryption methods, in this paper, we discussed the physical layer security features in large scale social networks. The opportunities in large scale social networks are summarized in physical, link and upper layer using the cross layer optimization, traditional physical layer security will meet new problems pending to be tackled. Also, we summarized the challenges due to some ideal considerations: the detection of wire-tap users, the utilization of high dynamic range and the information exchanging in cross layer design.

Cognitive radio (CR) is a novel technology for improving the utilization of radio electromagnetic spectrum. Recently, a new model called cognitive radio networks with cloud (CRNC) has been proposed to eliminate the constraints (pertinent to traditional CR) on power, memory space, and computational capacity. To enable multiple access of CRNC system, orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) has been investigated. Traditional OFDM-IDMA may not be applied to CRNC in a straightforward manner. This is because the spectral nulling in CR and the subsequent spectral sensing mismatch (between CR transmitter and receiver) could lead to unacceptable data error performance. This paper presents a new multiple access communication system, called transform domain communication system IDMA communication system, to deal with spectral nulling problems. Two improved schemes based on BPSK and cyclic code shift keying modulations are proposed. The proposed system structure integrates the advantages of both OFDM-IDMA and CRNC, which can make full use of other users' information while satisfying CR constraints. Simulation results demonstrate that the two proposed system architectures can achieve significant improvement on BER performance, multiple access capability, and anti-interference ability with spectral nulling.

We consider a three-node two-way relaying network, where an amplify-and-forward (AF) relay helps to forward the information of source nodes by using the energy harvested from the radio frequency signals transmitted from the two source nodes. The source and relay nodes work on half-duplex, which completes the transmission in two phases. Specifically, in the first transmission phase, two source nodes transmit their signals to relay. The received signals at relay will be divided into two subcarrier groups, to perform information decoding and energy harvesting separately. In the second transmission phase, after subcarrier pairing, relay amplifies the received signals then forwards them to the two source nodes with the harvested energy. An joint resource optimization problem, including subcarrier grouping, paring and power allocation, is formulated to maximize the sum transmission rate of source nodes with power constraints. Simulation analyses show that our proposed algorithm outperforms the other two benchmark algorithms and reveal the system performance influence of relay location and total transmission power.

In 5G wireless networks, the system capacity and the number of users will be significantly increased. However, the energy consumed for the communication will also be increased, which results in low system energy efficiency. Simultaneous wireless information and power transfer (SWIPT) can enable the user to perform energy harvesting from the radio-frequency signals parallel with the information decoding, which can effectively improve the system energy efficiency. In this paper, we study the energy efficiency optimization problem for the orthogonal frequency-division multiplexing-based 5G wireless networks with SWIPT, in which the subcarrier and power allocation are jointly optimized to maximize the system energy efficiency for single user and multiple user cases. For those two cases, through mathematical transforming, we use the Dinkelbach iterative method and the Lagrange dual method to solve the non-convex energy efficiency optimization problem. Simulation results show that compared with other algorithms, our proposed algorithm can achieve higher energy efficiency.

This paper is focused on training sequence design for efficient channel estimation in multiple-input multiple-output filterbank multicarrier (MIMO-FBMC) communications using offset quadrature amplitude modulation (OQAM). MIMO-FBMC is a promising technique to achieve high spectrum efficiency as well as strong robustness against dispersive channels due to its feature of time-frequency localization. A salient drawback of FBMC/OQAM signals is that only real-field orthogonality can be kept, leading to the intrinsic imaginary interference being a barrier for high-performance channel estimations. Also, conventional channel estimations in the MIMO-FBMC systems mostly suffer from high training overhead especially for large number of transmit antennas. Motivated by these problems, in this paper, we propose a new class of training sequences, which are formed by concatenation of two identical zero-correlation zone sequences whose auto-correlation and cross correlation are zero within a time-shift window around the in-phase position. Since only real-valued symbols can be transmitted in MIMO-FBMC systems, we propose “complex training sequence decomposition (CTSD)” to facilitate the reconstruction of the complex-field orthogonality of MIMO-FBMC signals. Our simulations validate that the proposed CTSD is an efficient channel estimation approach for practical preamble-based MIMO-FBMC systems.

Toward the era of mobile internet and Internet of Things (IoT), numerous sensors and devices are being introduced and interconnected. To support such amount of data traffic, traditional wireless communication technologies are facing challenges both in terms of increasingly shortage of spectrum resource and massive multiple access. In this paper, cognitive code division multiple access (Cognitive-CDMA) is proposed by combining the concept of cognitive radio with dynamic noncontinuous spectrum bands and code division multiple access. In order to suppress multiple access interference resulting from the non-orthogonality of partial available spectrum bins, carrier frequency offset, and spectrum sensing mismatch, an enhanced receiver design using frequency-domain oversampling (FDO) with linear minimum mean square error (MMSE) is considered in this paper for Cognitive-CDMA systems. By spectrum sensing to mitigate spectral interference in the transceiver and by utilizing FDO in the receiver to project received signal to a higher dimension, the proposed Cognitive-CDMA is able to support robust and massive multiple access for IoT applications. The simulation results show that the cognitive-CDMA with FDO-MMSE receiver outperforms that with conventional per-user MMSE receiver in the presence of multipath fading channels, carrier frequency offset, and spectrum sensing mismatch.

This paper aims to assess the risks of storage tank accidents and identify proper countermeasures. For these purposes, the causes and scope of tank accidents were discussed, and the scope of risk assessment was expanded from accident possibility and consequence severity to the hazard degree and vulnerability of the hazard-affected bodies. Then, the risk of storage tank accidents was evaluated from three aspects: accident possibility, hazard degree and vulnerability. Then, several risk control measures were put forward with the aim to control the hazard sources, block the propagation path and decrease vulnerability. After that, the proposed measures were validated through a case study on an oil tank farm of a coastal refinery in China. Specifically, a full-surface fire was simulated on one of the oil tanks in the tank farm, the effects of the fire on the adjacent tanks and pipe rack were observed, and several countermeasures were proposed to minimize the hazards. The research findings shed new light on the understanding and prevention of storage tank accidents.

Privacy and perspective are attracting more and more attention from worldwide users, so security in wireless communication system has become popular in recent researches, especially the physical layer security in 5G systems. In this article, we discuss the physical layer security in 5G downlink transmission systems; we propose the joint optimization of subcarrier, power, and artificial noise to achieve the higher security rate, our proposed method could approach to the upper bound of the security rate rather than the other methods, respectively. Then, in the simulation part, we present the theoretical and system-level simulation, and the results indicate that our proposed method could effectively increase the security rate without losing the system transmission speed.

Acoustic holography (AH) is a technique with significant potential in realms, such as biomedicine, industry, and augmented reality. The implementation of acoustic holograms can be realized by a passive approach or active ones. Although the passive approach (by a 3D printer) can achieve high-quality acoustic field generation, it is constrained by high manufacturing costs and static field control. On the contrary, the active approach with a phased transducer array (PTA) as the latest technique stands out since it supports dynamic, flexible, and reconfigurable acoustic field generation. However, current PTA-based AH techniques face the drawback of inferior acoustic field fineness due to the Spatial Bandwidth Product (SBP) limit of PTA, which hinders the application of PTA in precise tasks, such as neural electrodes and microfluidics control. To address this issue, we propose a super-resolution acoustic holography (SRAH) method inspired by the concept of super-resolution in ultrasonic imaging and computer vision, by which we can generate acoustic fields reaching the physical diffraction limit of acoustic waves regardless SBP of PTA. In other words, this method enables high-SBP acoustic field generation with low-SBP PTA. The method is based on self-supervised learning, integrating a generative adversarial network and a physical model of acoustic wave propagation, specifically the linear accumulation method. Both simulation and experimental results demonstrate that the proposed method can generate high-fidelity acoustic fields suitable for intricate tasks with low-SBP PTA. Moreover, the performance of the algorithm improves as the target SBP increases. Therefore, the proposed SRAH method shows great potential for applications requiring elaborate manipulation. [ABSTRACT FROM AUTHOR]

Towards the era of mobile Internet and the Internet of Things (IoT), numerous sensors and devices are being introduced and interconnected. To support such an amount of data traffic, traditional wireless communication technologies are facing challenges both in terms of the increasing shortage of spectrum resources and massive multiple access. The transform-domain communication system (TDCS) is considered as an alternative multiple access system, where 5G and mobile IoT are mainly focused. However, previous studies about TDCS are under the assumption that the transceiver has the global spectrum information, without the consideration of spectrum sensing mismatch (SSM). In this paper, we present the deterministic analysis of TDCS systems under arbitrary given spectrum sensing scenarios, especially the influence of the SSM pattern to the signal to noise ratio (SNR) performance. Simulation results show that arbitrary SSM pattern can lead to inferior bit error rate (BER) performance.