A guide RNA (gRNA) directs the function of a CRISPR protein effector to a target gene of choice, providing a versatile programmable platform for engineering diverse modes of synthetic regulation (edit, silence, induce, bind). However, the fact that gRNAs are constitutively active places limitations on the ability to confine gRNA activity to a desired location and time. To achieve programmable control over the scope of gRNA activity, here we apply principles from dynamic RNA nanotechnology to engineer conditional guide RNAs (cgRNAs) whose activity is dependent on the presence or absence of an RNA trigger. These cgRNAs are programmable at two levels, with the trigger-binding sequence controlling the scope of the effector activity and the target-binding sequence determining the subject of the effector activity. We demonstrate molecular mechanisms for both constitutively active cgRNAs that are conditionally inactivated by an RNA trigger (ON → OFF logic) and constitutively inactive cgRNAs that are conditionally activated by an RNA trigger (OFF → ON logic). For each mechanism, automated sequence design is performed using the reaction pathway designer within NUPACK to design an orthogonal library of three cgRNAs that respond to different RNA triggers. In E. coli expressing cgRNAs, triggers, and silencing dCas9 as the protein effector, we observe a median conditional response of ≈4-fold for an ON → OFF “terminator switch” mechanism, ≈15-fold for an ON → OFF “splinted switch” mechanism, and ≈3-fold for an OFF → ON “toehold switch” mechanism; the median crosstalk within each cgRNA/trigger library is <2%, ≈2%, and ≈20% for the three mechanisms. To test the portability of cgRNA mechanisms prototyped in bacteria to mammalian cells, as well as to test generalizability to different effector functions, we implemented the terminator switch in HEK 293T cells expressing inducing dCas9 as the protein effector, observing a median ON → OFF conditional response of ≈4-fold with median crosstalk of ≈30% for three orthogonal cgRNA/trigger pairs. By providing programmable control over both the scope and target of protein effector function, cgRNA regulators offer a promising platform for synthetic biology.

Purpose In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P < 0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.

Inkjet printing is a powerful technology for realizing high‐density pixelated perovskite light‐emitting diodes (PeLEDs). However, the coffee‐stain effect in the inkjet printing process often leads to uneven thickness and poor crystallization of printed perovskite features, which deteriorates the performance of PeLEDs. Here, a strategy is developed to suppress the coffee‐stain effect via enhancing Marangoni flow strength. An interfacial poly(vinylpyrrolidone) (PVP) layer is incorporated to tune the surface tension of the underlying hole transport layer (HTL) and enhance the perovskite crystallization. The substrate temperature is also carefully controlled to tune the printing solvent evaporation rate rationally. By optimizing the thickness of the PVP layer and the temperature of the printing stage, the coffee‐stain effect is dramatically restrained. In addition, the interfacial insulating PVP layers play a positive role in suppressing leakage current level of PeLEDs by avoiding any direct electrical contact between HTL and electron transporting layer. Finally, an inkjet‐printed PeLED with a brightness of 3640 cd m–2 and external quantum efficiency of 9.0% is achieved. This work highlights the availability of inkjet‐printing technology for fabricating patterned PeLEDs in information display applications.

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We report a new system for the silylation of aryl C–H bonds. The combination of [Ir(cod)(OMe)]2 and 2,9-Me2-phenanthroline (2,9-Me2-phen) catalyzes the silylation of arenes at lower temperatures and with faster rates than those reported previously, when the hydrogen byproduct is removed, and with high functional group tolerance and regioselectivity. Inhibition of reactions by the H2 byproduct is shown to limit the silylation of aryl C–H bonds in the presence of the most active catalysts, thereby masking their high activity. Analysis of initial rates uncovered the high reactivity of the catalyst containing the sterically hindered 2,9-Me2-phen ligand but accompanying rapid inhibition by hydrogen. With this catalyst, under a flow of nitrogen to remove hydrogen, electron-rich arenes, including those containing sensitive functional groups, undergo silylation in high yield for the first time, and arenes that underwent silylation with prior catalysts react over much shorter times with lower catalyst loadings. The synthetic value of this methodology is demonstrated by the preparation of key intermediates in the synthesis of medicinally important compounds in concise sequences comprising silylation and functionalization. Mechanistic studies demonstrate that the cleavage of the aryl C–H bond is reversible and that the higher rates observed with the 2,9-Me2-phen ligand are due to a more thermodynamically favorable oxidative addition of aryl C–H bonds.

Objectives To evaluate the application of a deep learning architecture, based on the convolutional neural network (CNN) technique, to perform automatic tumor segmentation of magnetic resonance imaging (MRI) for nasopharyngeal carcinoma (NPC). Materials and Methods In this prospective study, 87 MRI containing tumor regions were acquired from newly diagnosed NPC patients. These 87 MRI were augmented to >60,000 images. The proposed CNN network is composed of two phases: feature representation and scores map reconstruction. We designed a stepwise scheme to train our CNN network. To evaluate the performance of our method, we used case-by-case leave-one-out cross-validation (LOOCV). The ground truth of tumor contouring was acquired by the consensus of two experienced radiologists. Results The mean values of dice similarity coefficient, percent match, and their corresponding ratio with our method were 0.89±0.05, 0.90±0.04, and 0.84±0.06, respectively, all of which were better than reported values in the similar studies. Conclusions We successfully established a segmentation method for NPC based on deep learning in contrast-enhanced magnetic resonance imaging. Further clinical trials with dedicated algorithms are warranted.

Objectives The purpose of this study was to develop an automatic tracking method for the muscle cross‐sectional area (CSA) on ultrasound (US) images using a convolutional neural network (CNN). The performance of the proposed method was evaluated and compared with that of the state‐of‐the art muscle segmentation method. Methods A real‐time US image sequence was obtained from the rectus femoris muscle during voluntary contraction. A CNN was built to segment the rectus femoris muscle and calculate the CSA in each US frame. This network consisted of 2 stages: feature extraction and score map reconstruction. The training of the network was divided into 3 steps with output score map resolutions of one‐fourth, one‐half, and all of the original image. We evaluated the segmentation performance of our method with 5‐fold cross‐validation. The mean precision, recall, and dice similarity score were calculated. Results The mean precision, recall, and Dice's coefficient (DSC) ± SD were 0.936 ± 0.029, 0.882 ± 0.045, and 0.907 ± 0.023, respectively. Compared with the state‐of‐the‐art muscle segmentation method (constrained mutual‐information–based free‐form deformation), the proposed method using CNN showed high performance. Conclusions The automated method proposed in this study provides an accurate and efficient approach to the estimation of the muscle CSA during muscle contraction.

Matrix-enhanced delivery of cells is a promising approach to improving current cell therapies. Our objective was to create cell-laden composite microbeads that combine the attractive features of the natural polymers chitosan and fibrin. Liquid polydimethylsiloxane was used to emulsify a chitosan–fibrinogen solution containing suspended human fibroblast cells, followed by initiation of thrombin-mediated polymerization of fibrin and thermal/pH-mediated gelation of chitosan. Chitosan/fibrin weight percent (wt%) ratios of 100/0, 75/25, 50/50 and 25/75 were investigated. Microbead diameters ranged from 275 ± 99 μm to 38 ± 10 μm using impeller speeds from 600 to 1400 rpm. Fibroblasts remained viable on day 1 post-fabrication in all matrices, but cell viability was markedly higher in high-fibrin microbeads by day 8 post-fabrication. Cell spreading and interaction with the extracellular matrix was also markedly increased in high-fibrin matrices. Such composite microbeads containing viable entrapped cells have potential for minimally invasive delivery of cells for a variety of tissue repair applications.