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As the advancement of information security, human recognition as its core technology, has absorbed an increasing amount of attention in the past few years. A myriad of biometric features including fingerprint, face, iris, have been applied to security systems, which are occasionally considered vulnerable to forgery and spoofing attacks. Due to the difficulty of being fabricated, electrocardiogram (ECG) has attracted much attention. Though many works have shown the excellent human identification provided by ECG, most current ECG human identification (ECGID) researches only focus on rest situation. In this manuscript, we overcome the oversimplification of previous researches and evaluate the performance under both exercise and rest situations, especially the influence of exercise on ECGID. By applying various existing learning methods to our ECG dataset, we find that current methods which can well support the identification of individuals under rests, do not suffice to present satisfying ECGID performance under exercise situations, therefore exposing the deficiency of existing ECG identification methods.

Human carboxylesterase 2 (hCE2), one of the major carboxylesterases in the human intestine and various tumour tissues, plays important roles in the oral bioavailability and treatment outcomes of ester- or amide-containing drugs or prodrugs, such as anticancer agents CPT-11 (irinotecan) and LY2334737 (gemcitabine). In this study, 18β-glycyrrhetinic acid (GA), the most abundant pentacyclic triterpenoid from natural source, was selected as a reference compound for the development of potent and specific inhibitors against hCE2. Simple semi-synthetic modulation on GA was performed to obtain a series of GA derivatives. Structure-activity relationship analysis brought novel insights into the structure modification of GA. Converting the 11-oxo-12-ene of GA to 12-diene moiety, and C-3 hydroxyl and C-30 carboxyl group to 3-O-β-carboxypropionyl and ethyl ester respectively, led to a significant enhancement of the inhibitory effect on hCE2 and the selectivity over hCE1. These exciting findings inspired us to design and synthesize the more potent compound 15 (IC50 0.02 μM) as a novel and highly selective inhibitor against hCE2, which was 3463-fold more potent than the parent compound GA and demonstrated excellent selectivity (>1000-fold over hCE1). The molecular docking study of compound 15 and the active site of hCE1 and hCE2 demonstrated that the potent and selective inhibition of compound 15 toward hCE2 could partially be attributed to its relatively stronger interactions with hCE2 than with hCE1.

Fructus Psoraleae (FP) is an edible Chinese herbal which is widely used in Asia for the treatment of various diseases including asthma, diarrhea, and osteoporosis. This study aimed to investigate the inhibitory effects of the crude ethanol extract from FP on human carboxylesterase 2 (hCE2), as well as to identity and characterize the naturally occurring inhibitors of hCE2 in FP. Our results demonstrated that the ethanol extract of FP displayed potent inhibitory effects towards hCE2, while five major bioactive constitutes in FP were efficiently identified by LC-DAD-ESI-MS/MS, with the aid of LC-based activity profiling. The identified bioactive compounds including neobavaisoflavone, isobavachalcone, bavachinin, corylifol A and bakuchiol were found to be naturally occurring potent inhibitors of hCE2, with low Ki values ranging from 0.62μM to 3.89μM. This is the first report of the chemical constitutes in FP as potent inhibitors of hCE2.

The classification accuracy of electrocardiogram signal is often affected by diverse factors in which mislabeled training samples issue is one of the most influential problems. In order to mitigate this negative effect, the method of cross validation is introduced to identify the mislabeled samples. The method utilizes the cooperative advantages of different classifiers to act as a filter for the training samples. The filter removes the mislabeled training samples and retains the correctly labeled ones with the help of 10-fold cross validation. Consequently, a new training set is provided to the final classifiers to acquire higher classification accuracies. Finally, we numerically show the effectiveness of the proposed method with the MIT-BIH arrhythmia database.

A charge-pump phase locked loop(CPPLL) is designed for high-precision and high-stability in micro-inertial system. In this CPPLL, D-flip-flops are used to eliminate dead phase-zone and current mirrors are designed to improve matching between charge and discharge of the charge-pump, besides, unit gain amplifiers are applied to suppress the charge sharing. The simulation result shows that the frequency stability achieves 0.186/10000, while the periodic jitter is 931.32ps.