We report the unexpected radial migration of DNA molecules in capillary electrophoresis (CE) with applied Poiseuille flow. Such movement can contribute to anomalous migration times, peak dispersion, and size and shape selectivity in CE. When Poiseuille flow is applied from the cathode to the anode, DNA molecules move toward the center of the capillary, forming a narrow, highly concentrated zone. Conversely, when the flow is applied from the anode to the cathode, DNA molecules move toward the walls, leaving a DNA-depleted zone around the axis. We showed that the deformation and orientation of DNA molecules under Poiseuille flow was responsible for the radial migration. By analyzing the forces acting on the deformed and oriented DNA molecules, we derived an expression for the radial lift force, which explained our results very well under different conditions with Poiseuille flow only, electrophoresis only, and the combination of Poiseuille flow and electrophoresis. Factors governing the direction and velocity of radial migration were elucidated. Potential applications of this phenomenon include an alternative to sheath flow in flow cytometry, improving precision and reliability of single-molecule detection, reduction of wall adsorption, and size separation with a mechanism akin to field-flow fractionation. On the negative side, nonuniform electroosmotic flow along the capillary or microfluidic channel is common in CE, and radial migration of certain analytes cannot be neglected.

Under specific experimental conditions, the electrokinetic separation of certain microorganisms can produce peaks of very high apparent efficiencies (approximately 10(6)-10(10) theoretical plates/m). This is unusual in that no deliberate focusing mechanism was employed. To investigate this process further, the separation was monitored in real time using a charge-coupled device (CCD) imaging system. At least two different processes seem to be operative when these narrow peaks are observed. The initial field-induced association of cells appears to require a dilute polymer solution, electroosmotic flow (preferably countercurrent to the direction of cell electrophoresis), and a direct current electric field. Three possible models are presented that may explain aspects of the observed behavior. The balance between dispersive forces and intercellular adhesive forces also affects the observed bandwidths. Understanding and controlling the dynamic and aggregation of cells in microfluidic processes is essential, since it can be beneficial for some experiments and detrimental to others.

Well-aligned ZnO/ZnSe core/shell nanowire arrays with type-II energy alignment are synthesized via a two-step chemical vapor deposition method. Morphology and structure studies reveal a transition layer of wurtzite ZnSe between the wurtzite ZnO core and the cubic ZnSe shell. Type-II interfacial transitions are observed in the spectral region from visible to near infrared in transmission and photoluminescence. More significantly, for the first time, the interfacial transition is shown to extend the photoresponse of the prototype photovoltaic device based on the coaxial nanowire array to a threshold much below the bandgap of either component (3.3 and 2.7 eV, respectively) at 1.6 eV, with an external quantum efficiency of $4% at 1.9 eV and 9.5% at 3 eV. These results represent a major advance towards the realization of all-inorganic type-II heterojunction photovoltaic devices in an optimal device architecture.

This study verifies the feasibility of using deep-learning algorithms for the binary classification as normal or abnormal of standard fetal ultrasound brain images in axial planes. What are the clinical implications of this work? Using deep-learning algorithms could help to reduce false-negative diagnoses. They are expected to help solve the shortage of sonologists for basic prenatal ultrasound in China and worldwide. This study lays the foundation for further multiclassification research on the diagnosis of fetal intracranial abnormalities and differential diagnosis using deep learning.

We demonstrate a novel separation mechanism for large molecules based on their radial migration in capillary electrophoresis with applied hydrodynamic flow (HDF). The direction of radial migration depends on the direction of the applied HDF relative to the electric field. The radial migration velocities are size-dependent, which could be attributed to the different degree of deformation under shear flow. Analytical separation was demonstrated on a sample plug containing lambda DNA (48 502 bp) and phiX174 RF DNA (5386 bp) with baseline separation. Alternatively, this separation mode can be performed continuously and is thus applicable to preparative separations. Without the need for gel/polymer or complex instrumentation, this separation technique is complementary to capillary gel electrophoresis and field-flow fractionation. Although large DNA molecules were used to demonstrate the separation mechanism here, these protocols could also be applied to the separation of proteins, cells, or particles based on size, shape, or deformability.

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We studied the aggregation of a rod-shaped bacteria, Bifidobacterium infantis, during capillary electrophoresis (CE). A microscope with an intensified CCD camera was employed to monitor the migration and aggregation of bacteria, which are labeled with fluorescent dye Syto 9 and excited with a 488-nm argon ion laser. A collision-based aggregation mechanism is proposed, in which collisions between microbes result from different mobilities and migration directions in the electric field. Individual microbes are aligned differently with respect to the direction of the electric field and exhibit different drag coefficients. The long-range forces include van der Waals attraction and electrostatic repulsion as qualitatively described by DLVO theory. Collisions in CE produce sufficient energy to overcome electrostatic repulsion, thus improving the efficiency of aggregation. This is supported by the fact that higher electric fields always resulted in faster aggregation. Also, when sodium phosphate buffer was used, increasing the ionic strength resulted in faster aggregation. However, when Tris-boric acid-EDTA (TBE, pH 9.1) buffer was used, the aggregation speed decreased when the ionic strength increased. We attribute this to the change of the surface of the bacteria at high borate and EDTA concentration, such as the loss of polysaccharides or the presence of complexation. This reduces the hydrophobicity of the surface and, thus, the short-range attractive forces. The addition of 0.05% poly(ethylene oxide) (PEO) into high ionic strength TBE buffer increased the aggregation rate. This can be attributed to the bridging effect of PEO between microbes. Further increase in the concentration of polymer reduced the aggregation rate, especially when the electric field was low, due in part to the increase in viscosity. The decrease in migration velocity produced lower collision energies and lower aggregation efficiencies as well.

What are the novel findings of this work?In this study, we developed and validated an artificial intelligence system, the Prenatal ultrasound diagnosis Artificial Intelligence Conduct System (PAICS), to detect nine specific intracranial-malformation patterns in standard sonographic reference planes of the fetal central nervous system (CNS). The PAICS achieved excellent performance on both internal and external validation, with accuracy comparable to that of expert sonologists, while requiring significantly less time. What are the clinical implications of this work?The PAICS is a real-time artificial intelligence-aided image recognition system capable of detecting fetal intracranial malformations. This fast, accurate algorithm has the potential to be an effective and efficient tool in screening for congenital CNS malformations. It should be particularly useful in community hospitals, which often lack imaging expertise.