Predicting which genes will respond to transcription factor perturbations

The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene\u27s expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer

Reliable diameter control of carbon nanotube nanowires using withdrawal velocity

Carbon nanotube (CNT) nanobundles are widely used in nanoscale imaging, fabrication, and electrochemical and biological sensing. The diameter of CNT nanobundles should be controlled precisely, because it is an important factor in determining electrode performance. Here, we fabricated CNT nanobundles on tungsten tips using dielectrophoresis (DEP) force and controlled their diameters by varying the withdrawal velocity of the tungsten tips. Withdrawal velocity pulling away from the liquid-air interface could be an important, reliable parameter to control the diameter of CNT nanobundles. The withdrawal velocity was controlled automatically and precisely with a one-dimensional motorized stage. The effect of the withdrawal velocity on the diameter of CNT nanobundles was analyzed theoretically and compared with the experimental results. Based on the attachment efficiency, the withdrawal velocity is inversely proportional to the diameter of the CNT nanobundles; this has been demonstrated experimentally. Control of the withdrawal velocity will play an important role in fabricating CNT nanobundles using DEP phenomena.110Ysciescopu

One-directional flow of ionic solutions along fine electrodes under an alternating current electric field

Electric fields are widely used for controlling liquids in various research fields. To control a liquid, an alternating current (AC) electric field can offer unique advantages over a direct current (DC) electric field, such as fast and programmable flows and reduced side effects, namely the generation of gas bubbles. Here, we demonstrate one-directional flow along carbon nanotube nanowires under an AC electric field, with no additional equipment or frequency matching. This phenomenon has the following characteristics: First, the flow rates of the transported liquid were changed by altering the frequency showing Gaussian behaviour. Second, a particular frequency generated maximum liquid flow. Third, flow rates with an AC electric field (approximately nanolitre per minute) were much faster than those of a DC electric field (approximately picolitre per minute). Fourth, the flow rates could be controlled by changing the applied voltage, frequency, ion concentration of the solution and offset voltage. Our finding of microfluidic control using an AC electric field could provide a new method for controlling liquids in various research fields

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