Mechanisms and control measures of low temperature storage-induced chilling injury to solanaceous vegetables and fruits.

Low temperature storage is widely used for storage and transportation of fruits and vegetables after harvest. As a cold-sensitive fruit vegetable, post-harvest solanaceous vegetables and fruits are susceptible to chilling injury during low temperature storage, which reduces its sensory quality and edible quality and shortens its storage period, thus leading to huge economic losses. Therefore, it is an essential to clarify the occurrence mechanism of chilling injury caused by low temperature storage in solanaceous vegetables and fruits, and to propose corresponding prevention and control measures for chilling injury.

Targeting CD123 in blastic plasmacytoid dendritic cell neoplasm using allogeneic anti-CD123 CAR T cells.

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematologic malignancy with poor outcomes with conventional therapy. Nearly 100% of BPDCNs overexpress interleukin 3 receptor subunit alpha (CD123). Given that CD123 is differentially expressed on the surface of BPDCN cells, it has emerged as an attractive therapeutic target.

Atmospheric fine particles in a typical coastal port of Yangtze River Delta.

In recent decades, coastal ports have experienced rapid development and become an important economic and ecological hub in China. Atmospheric particle is a research hotspot in atmospheric environmental sciences in inland regions. However, few studies on the atmospheric particle were conducted in coastal port areas in China, which indeed suffers atmospheric particle pollution.

Sodium-glucose cotransporter 2 inhibition suppresses HIF-1α-mediated metabolic switch from lipid oxidation to glycolysis in kidney tubule cells of diabetic mice.

Inhibition of sodium-glucose cotransporter 2 (SGLT2) in the proximal tubule of the kidney has emerged as an effective antihyperglycemic treatment. The potential protective role of SGLT2 inhibition on diabetic kidney disease (DKD) and underlying mechanism, however, remains unknown. In this study, metabolic switch was examined using kidney samples from human with diabetes and streptozocin (STZ)-induced experimental mouse model of diabetes treated with or without SGLT2 inhibitor dapagliflozin.

Assessment of the Impacts of Leaflet Design on the Hemodynamic Characteristics of ePTFE Pulmonary Prosthetic Valves.

Prosthetic pulmonary valves are widely used in the management procedures of various congenital heart diseases, including the surgical pulmonary valve replacement (PVR) and right ventricular outflow tract reconstruction (RVOT). The discouraging long-term outcomes of standard prostheses, including homografts and bioprosthetic, constrained their indications. Recent developments in the expanded-polytetrafluoroethylene (ePTFE) pulmonary prosthetic valves provide promising alternatives.

UCP2-dependent improvement of mitochondrial dynamics protects against acute kidney injury.

Acute kidney injury (AKI) is a public health concern, with high morbidity and mortality rates in hospitalized patients and because survivors have an increased risk of progression to chronic kidney disease. Mitochondrial damage is the critical driver of AKI-associated dysfunction and loss of tubular epithelial cells; however, the pathways that mediate these events are poorly defined. Here, in murine ischemia/reperfusion (I/R)-induced AKI, we determined that mitochondrial damage is associated with the level of renal uncoupling protein 2 (UCP2).

Feature extraction and recognition of ictal EEG using EMD and SVM.

Automatic seizure detection is significant for long-term monitoring of epilepsy, as well as for diagnostics and rehabilitation, and can decrease the duration of work required when inspecting the EEG signals. In this study we propose a novel method for feature extraction and pattern recognition of ictal EEG, based upon empirical mode decomposition (EMD) and support vector machine (SVM). First the EEG signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and then the coefficient of variation and fluctuation index of IMFs are extracted as features.

EEG classification approach based on the extreme learning machine and wavelet transform.

Automatic detection and classification of electroencephalogram (EEG) epileptic activity aid diagnosis and relieve the heavy workload of doctors. This article presents a new EEG classification approach based on the extreme learning machine (ELM) and wavelet transform (WT). First, the WT is used to extract useful features when certain scales cover abnormal components of the EEG.

EEG non-linear feature extraction using correlation dimension and Hurst exponent.

In this work, we evaluated the differences between epileptic electroencephalogram (EEG) and interictal EEG by computing some non-linear features. Correlation dimension (CD) and Hurst exponent (H) were calculated for 100 segments of epileptic EEG and 100 segments of interictal EEG. A comparison was made between epileptic EEG and interictal EEG in those non-linear parameters.

Epileptic EEG classification based on extreme learning machine and nonlinear features.

The automatic detection and classification of epileptic EEG are significant in the evaluation of patients with epilepsy. This paper presents a new EEG classification approach based on the extreme learning machine (ELM) and nonlinear dynamical features. The theory of nonlinear dynamics has been a powerful tool for understanding brain electrical activities.