Compressed sensing (CS) has been used in dynamic cardiac MRI to reduce the data acquisition time. The sparseness of the dynamic image series in the spatial and temporal-frequency (x-f) domain has been exploited in existing works. In this paper, we propose a new k-t Iterative Support Detection (k-t ISD) method to improve the CS reconstruction for dynamic cardiac MRI by incorporating additional information on the support of the dynamic image in x-f space based on the theory of CS with partially known support. The proposed method uses an iterative procedure for alternating between image reconstruction and support detection in x-f space. At each iteration, a truncated ℓ1 minimization is applied to obtain the reconstructed image in x-f space using the support information from the previous iteration. Subsequently, by thresholding the reconstruction, we update the support information to be used in the next iteration. Experimental results demonstrate that the proposed k-t ISD method improves the reconstruction quality of dynamic cardiac MRI over the basic CS method in which support information is not exploited.

Orthogonal Time Frequency Space (OTFS) is a novel modulation scheme designed in the Doppler-delay domain to fully exploit time and frequency diversity of general timevarying channels. In this paper, we present a novel discrete-time analysis of OFDM-based OTFS transceiver with a concise and vectorized input-output relationship that clearly characterizes the contribution of each underlying signal processing block in such systems. When adopting cyclic prefix in the time domain, our analysis reveals that the proposed MIMO OTFS and OFDM systems have the same ergodic capacity despite the well-known fact that the former has great advantages in low-complexity receiver design for high Doppler channels. The proposed discretetime vectorized formulation is applicable to general fast fading channels with arbitrary window functions. It also enables practical low-complexity receiver design for which such a concise formulation of the input-output relationship is of great benefits.

Purpose Frequent exacerbators are a specific phenotype of chronic obstructive pulmonary disease (COPD), whose clinical characteristics and prognostic biomarkers during severe acute exacerbation (AECOPD) have not yet been fully elucidated. The aim of this study was to investigate the clinical features of severe AECOPD in frequent exacerbators and explore the predictive value of the neutrophil-to-lymphocyte ratio (NLR) for outcome in this phenotype during severe exacerbation. Patients and Methods A total of 604 patients with severe AECOPD were retrospectively included in the study. Subjects were defined as frequent exacerbators if they experienced two or more exacerbations in the past year. Clinical characteristics and worse outcome (ICU admission, or invasive ventilation, or in-hospital mortality) during severe AECOPD were compared between frequent exacerbators and non-frequent ones. Furthermore, the relationship between NLR and worse outcome in frequent exacerbators was analyzed using logistic regression and receiver operating characteristic (ROC). Results Among 604 patients with severe AECOPD, 282 (46.69%) were frequent exacerbators and 322 (53.31%) were non-frequent exacerbators. Compared with the non-frequent ones, frequent exacerbators presented higher levels of NLR (5.93 [IQR, 3.40–9.28] vs 4.41 [IQR, 2.74–6.80]; p <0.001), and more worse outcome incidence (58 [20.57%] vs 38 [11.80%]; p =0.003). Moreover, among the frequent exacerbators, NLR levels in the patients with worse outcome were much higher than in those without worse outcome (11.09 [IQR, 7.74–16.49] vs 5.28 [IQR, 2.93–7.93]; p <0.001). Increased NLR was significantly associated with a higher risk of worse outcome in frequent exacerbators (OR, 1.43; 95% CI, 1.28–1.64; p <0.001). Furthermore, ROC analysis revealed that a cut-off value of 10.23, NLR could predict worse outcome of severe AECOPD in frequent exacerbators (sensitivity 62.1%, specificity 92.0%, AUC 0.833). Conclusion Frequent exacerbators exhibited an increased level of NLR and a higher proportion of worse outcome during severe AECOPD. NLR is expected to be a promising predictive biomarker for the prognosis of severe AECOPD in frequent exacerbators.

The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods.

The genus Cyrtonotula Uvarov, 1939 (Blaberidae, Epilamprinae) is recorded for the first time from Hainan Island, China. Three new species, Cyrtonotula epunctata Wang &amp; Wang, sp. nov., C. maculosa Wang &amp; Wang, sp. nov., and C. longialata Wang &amp; Wang, sp. nov., are described based on morphological data and a molecular analysis using Automatic Barcode Gap Discovery (ABGD). Additional barcode data of blaberid species, including these three new species, are provided to facilitate future species identification. Morphological photographs and habitat photos of these new species, as well as a key to the known species, are provided.

In machine olfaction or electronic nose, sensor optimization is important to enhance pattern recognition efficiency and reduce redundant information. Highly correlated response of one sensor to two different odors implies less contribution of this sensor to the classification of these two odors. Variance difference is a significant index to measure the similarity of sensor responses. A sensor optimization method based on variance difference is proposed in this paper; both the average value of variance difference and cluster analysis of variance difference matrix were considered to identify several possible sensor subsets. Six Chinese herbal medicines and linear discrimination analysis (LDA) were applied to test the classification results in order to determine the best subset. LDA results indicated that the optimized sensor subset performed well in classification of the six Chinese medicines. The proposed sensor array optimization method could be applied to other kinds of odors classification as a novel method.

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Objectives: The impact of ineffective esophageal motility (IEM) on gastroesophageal reflux disease (GERD) remains unknown, and abnormal esophageal motility often coexists with abnormal gastric motility. We aimed to investigate the role of IEM in GERD and its relationship with gastric electrical activity.Methods: Patients diagnosed as GERD based on GERD-questionnaire score ≥8 in our hospital from January 2020 to June 2022 were included. All patients underwent 24-h multichannel intraluminal impedance-pH monitoring, high-resolution manometry, and electrogastrogram and were categorized into the normal esophageal motility (NEM) and IEM groups, respectively. Reflux characteristics and gastric electric activity were compared between the two groups, and the correlation between gastric electric activity and reflux was analyzed.Results: Acid exposure time, total reflux episodes, and DeMeester score in the IEM group were higher than those in the NEM group. Distal mean nocturnal baseline impedance was significantly lower in the IEM group. Compared with the NEM group, the power ratio (PR) of fundus, antrum and pylorus and premeal and postmeal normal wave ratio of antrum were significantly lower in IEM. The total reflux episodes were negatively correlated with the PR of fundus and pylorus, and the DeMeester score was negatively correlated with the PR of corpus and pylorus.Conclusions: IEM may lead to increased reflux, resulting in esophageal mucosal damage.There may be consistency between abnormal esophageal motility and gastric motility.