Opportunities and challenges for deep learning in cell dynamics research

With the growth of artificial intelligence (AI), there has been an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes, but it has also started supporting advances in drug development, precision medicine and genome-phenome mapping. Here we survey existing AI-based techniques and tools, and open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from the computational perspective and review emerging research frontiers and innovative applications for deep learning-guided automation for cell dynamics research

Enhanced Thermoelectric Properties in Bulk Nanowire Heterostructure-Based Nanocomposites through Minority Carrier Blocking

To design superior thermoelectric materials the minority carrier blocking effect in which the unwanted bipolar transport is prevented by the interfacial energy barriers in the heterogeneous nanostructures has been theoretically proposed recently. The theory predicts an enhanced power factor and a reduced bipolar thermal conductivity for materials with a relatively low doping level, which could lead to an improvement in the thermoelectric figure of merit (ZT). Here we show the first experimental demonstration of the minority carrier blocking in lead telluride–silver telluride (PbTe–Ag_2Te) nanowire heterostructure-based nanocomposites. The nanocomposites are made by sintering PbTe–Ag_2Te nanowire heterostructures produced in a highly scalable solution-phase synthesis. Compared with Ag_2Te nanowire-based nanocomposite produced in similar method, the PbTe–Ag_2Te nanocomposite containing ∼5 atomic % PbTe exhibits enhanced Seebeck coefficient, reduced thermal conductivity, and ∼40% improved ZT, which can be well explained by the theoretical modeling based on the Boltzmann transport equations when energy barriers for both electrons and holes at the heterostructure interfaces are considered in the calculations. For this p-type PbTe–Ag_2Te nanocomposite, the barriers for electrons, that is, minority carriers, are primarily responsible for the ZT enhancement. By extending this approach to other nanostructured systems, it represents a key step toward low-cost solution-processable nanomaterials without heavy doping level for high-performance thermoelectric energy harvesting