We studied by questionnaire 530 subjects with chronic myeloid leukaemia (CML) in Hubei Province during the recent SARS-CoV-2 epidemic. Five developed confirmed (N = 4) or probable COVID-19 (N = 1). Prevalence of COVID-19 in our subjects, 0.9% (95% Confidence Interval, 0.1, 1.8%) was ninefold higher than 0.1% (0, 0.12%) reported in normals but lower than 10% (6, 17%) reported in hospitalised persons with other haematological cancers or normal health-care providers, 7% (4, 12%). Co-variates associated with an increased risk of developing COVID-19 amongst persons with CML were exposure to someone infected with SARS-CoV-2 (P = 0.037), no complete haematologic response (P = 0.003) and comorbidity(ies) (P = 0.024). There was also an increased risk of developing COVID-19 in subjects in advanced phase CML (P = 0.004) even when they achieved a complete cytogenetic response or major molecular response at the time of exposure to SARS-CoV-2. 1 of 21 subjects receiving 3rd generation tyrosine kinase-inhibitor (TKI) developed COVID-19 versus 3 of 346 subjects receiving imatinib versus 0 of 162 subjects receiving 2nd generation TKIs (P = 0.096). Other co-variates such as age and TKI-therapy duration were not significantly associated with an increased risk of developing COVID-19. Persons with these risk factors may benefit from increased surveillance of SARS-CoV-2 infection and possible protective isolation.

The development of electrochemical methods for enantioselective recognition is a focus of research in pharmaceuticals and biotechnology. In this study, a pair of water-soluble chiral 3,4-ethylenedioxythiophene (EDOT) derivatives, (R)-2'-hydroxymethyl-3,4-ethylenedioxythiophene ((R)-EDTM) and (S)-2'-hydroxymethyl-3,4-ethylenedioxythiophene ((S)-EDTM), were synthesized and electrodeposited on the surface of a glassy carbon electrode (GCE) via current-time (I-t) polymerization in an aqueous LiClO electrolyte. These chiral PEDOT polymers were used to fabricate chiral sensors and to investigate the enantioselective recognition of d-/l-3,4-dihydroxyphenylalanine, d-/l-tryptophan, and (R)-/(S)-propranolol enantiomers, respectively. The results indicated that the (R)-PEDTM/GCE sensor showed a higher peak current response toward the levo or (S) forms of the tested enantiomers, while the opposite phenomenon occurred for (S)-PEDTM/GCE. The mechanism of the stereospecific interaction between these enantiomers and the chiral polymers was determined. Therefore, a model of the chiral recognition by the chiral conducting polymer electrodes and an electrochemical method was proposed. The chirality of the enantiomers was confirmed by two parameters: the chirality of the electrode and the peak current response. These findings pave the way for the application of chiral PEDOT as electrode modification material in the electrochemical chiral recognition field.

Dimensionality reduction on Riemannian manifolds is challenging due to the complex nonlinear data structures. While probabilistic principal geodesic analysis (PPGA) has been proposed to generalize conventional principal component analysis (PCA) onto manifolds, its effectiveness is limited to data with a single modality. In this paper, we present a novel Gaussian latent variable model that provides a unique way to integrate multiple PGA models into a maximum-likelihood framework. This leads to a well-defined mixture model of probabilistic principal geodesic analysis (MPPGA) on sub-populations, where parameters of the principal subspaces are automatically estimated by employing an Expectation Maximization algorithm. We further develop a mixture Bayesian PGA (MBPGA) model that automatically reduces data dimensionality by suppressing irrelevant principal geodesics. We demonstrate the advantages of our model in the contexts of clustering and statistical shape analysis, using synthetic sphere data, real corpus callosum, and mandible data from human brain magnetic resonance (MR) and CT images.

During winters, the high-speed train travels in the northern of China is struck by to the snow, ice and coldness, massive snow accumulating on the bogies. To understand the cause of snow packing on the highspeed train's bogies clearly, the 3-D unsteady Reynolds-averaged Navier-Stokes equations with a RNG double-equations turbulence model and a DPM discrete phase model were used to investigate the flow field carried snow particles in a single high-speed train bogie region and monitor the movement of snow particles. And, the numerical simulation was verified by the wind tunnel test. The results show that when air flows into the region, the airflow will rise and impact on the wheels, brakes, electromotors and other parts of bogie regions. The snow particles will follow the air, while the air direction changes sharply the particles will keep the movement due to the inertia. Afterwards, the snow packs on the bogie. In front of the bogies the streamlines of the air and the particle path lines are basically the same. However, due to the inertia of mass particles, the following characteristics of the snow particles with the air are not obvious in the bogie leeward side. Different structures of the end plates will affect the snow accumulation in the bogie regions.