https://www.selleckchem.com/products/ym201636.html To enable highly conductive electronic textiles (E-textiles), we herein demonstrate a simple solution treatment of poly (3,4-ethylenedioxythiophene) poly (styrene sulfonate) (PEDOTPSS)-coated textiles by dimethyl sulfoxide (DMSO) and methanol. The subsequent solution engineering of DMSO and methanol not only enhances crystallization of PEDOT chains but also the contact for PEDOTPSS to the fibers. Additionally, the methanol dipping effectively removes the insulating PSS part from the conductive PEDOT chains, which contributes to subsequently reduced sheet resistance of less than 3 Ω/sq of the conductive textiles. Joule heating property of the highly conductive textiles achieves the maximum temperature with the temperature reaching 133 °C at a low applied voltage of 3 V within 20 s, which promises highly conductive E-textiles as multi-functional wearable heater applications.Model quality assessment (MQA), which selects near-native structures from structure models, is an important process in protein tertiary structure prediction. The three-dimensional convolution neural network (3DCNN) was applied to the task, but the performance was comparable to existing methods because it used only atom-type features as the input. Thus, we added sequence profile-based features, which are also used in other methods, to improve the performance. We developed a single-model MQA method for protein structures based on 3DCNN using sequence profile-based features, namely, P3CMQA. Performance evaluation using a CASP13 dataset showed that profile-based features improved the assessment performance, and the proposed method was better than currently available single-model MQA methods, including the previous 3DCNN-based method. We also implemented a web-interface of the method to make it more user-friendly.Background and Objectives Despite advances in treatment, local recurrence remains a great concern in patients with rectal cancer. The aim of t