Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can be denoted by a multi-way array called as tensor. This review summarizes the current progress of tensor decomposition of EEG signals with three aspects. The first is about the existing modes and tensors of EEG signals. Second, two fundamental tensor decomposition models, canonical polyadic decomposition (CPD, it is also called parallel factor analysis-PARAFAC) and Tucker decomposition, are introduced and compared. Moreover, the applications of the two models for EEG signals are addressed. Particularly, the determination of the number of components for each mode is discussed. Finally, the N-way partial least square and higher-order partial least square are described for a potential trend to process and analyze brain signals of two modalities simultaneously.

The purpose of this study was to review the clinical features of maxillofacial space infection (MSI), and to identify the potential risk factors predisposing to life-threatening complications. A retrospective review of the medical charts of patients with MSI treated at Peking University School and Hospital of Stomatology from August 2008 to September 2013 was conducted. A total of 127 patients [75 men (59.1%) and 52 women (40.9%); mean age, 45.39 ± 21.18 years, with a range of 1–85 years] formed the study cohort. The most common cause of MSI was odontogenic infection (57.5%). The most common space involved was the submandibular space. All patients were treated by antibiotics as well as surgical incision and drainage. Sixteen patients developed life-threatening complications, and the dominant complication was respiratory obstruction. Multivariate logistic regression analysis revealed the percentage of neutrophils (NEUT%) upon hospital admission ≥85.0% to be associated with life-threatening complications (P < 0.05). Even though adequate antibiotic therapy and incision and drainage of abscess were given, MSI patients with NEUT% upon hospital admission ≥85.0% carry a higher risk of life-threatening complications. In these patients, an aggressive treatment strategy is mandatory.

One endpoint of clinical islet cell transplantation for type 1 diabetic patients is the elimination or reduction of hypoglycemia. We previously developed a simple tool to evaluate islet graft function: the secretory unit of islet transplant objects (SUITO) index. The aim of this study is to clarify the association between the SUITO index and hypoglycemic episodes. Data from 310 clinical evaluations of 11 islet recipients were included in this study. Fasting plasma C-peptide and glucose levels were measured at every evaluation. The SUITO index was calculated according to the following formula: 1500 × C-peptide level (ng/ml)/[blood glucose level (mg/dl) - 63]. The number of hypoglycemic events (<3.8 mmol/L) and severe hypoglycemic events (<2.2 mmol/L or hypoglycemic unawareness) was assessed on the basis of interviews and self-monitoring of blood glucose (SMBG). Receiver operating characteristic (ROC) analysis was performed to determine the cut-off values of the SUITO index for hypoglycemic events. Based on the ROC study, follow-up data after transplantations were divided into the following three groups: low-SUITO (SUITO index <10, n = 91), middle-SUITO (10 ≤SUITO index <26, n = 83), high-SUITO (SUITO index ≤26, n = 125). The frequency of total hypoglycemia in the high-SUITO group was significantly decreased when compared to the other groups (value with Kruskal-Wallis test p < 0.001). The frequency of total severe hypoglycemia was significantly decreased in the low-SUITO group compared to pretransplant status and further decreased in the middle- and high-SUITO group. Spearman correlation coefficients were -0.663 (p < 0.001) between the number of total hypoglycemic events per one month and the SUITO index and -0.521 (p < 0.001) between that of severe events and the SUITO index. The SUITO index could predict the severity of hypoglycemic episodes in type 1 diabetic patients who received islet cell transplantations.