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Although the developmental trajectory of pancreatic lineages is generally conserved between humans and mice, clear interspecies differences, including differences in the proportions of cell types and the regulatory networks associated with the differentiation of specific lineages, have been detected. Our findings support a model in which sequential transient progenitor cell states determine the differentiation of multiple cell lineages and provide a blueprint for directing the generation of pancreatic islets in vitro.Peripheral nerve injury could lead to chronic neuropathic pain. Understanding transcriptional changes induced by nerve injury could provide fundamental insights into the complex pathogenesis of neuropathic pain. Gene expression profiles of dorsal root ganglia (DRG) in neuropathic pain condition have been studied. However, little is known about transcriptomic changes in individual DRG neurons after peripheral nerve injury. Here we performed single-cell RNA sequencing on dissociated mouse DRG cells after spared nerve injury (SNI). In addition to DRG neuron types that are found under physiological conditions, we identified three SNI-induced neuronal clusters (SNIICs) characterized by the expression of Atf3/Gfra3/Gal (SNIIC1), Atf3/Mrgprd (SNIIC2) and Atf3/S100b/Gal (SNIIC3). These SNIICs originated from Cldn9+/Gal+, Mrgprd+ and Trappc3l+ DRG neurons, respectively. Interestingly, SNIIC2 switched to SNIIC1 by increasing Gal and reducing Mrgprd expression 2 days after nerve injury. Inferring the gene regulatory networks after nerve injury, we revealed that activated transcription factors Atf3 and Egr1 in SNIICs could enhance Gal expression while activated Cpeb1 in SNIIC2 might suppress Mrgprd expression within 2 days after SNI. Furthermore, we mined the transcriptomic changes in the development of neuropathic pain to identify potential analgesic targets. We revealed that cardiotrophin-like cytokine factor 1, which activates astrocytes in the dorsal horn of spinal cord, was upregulated in SNIIC1 neurons and contributed to SNI-induced mechanical allodynia. Therefore, our results provide a new landscape to understand the dynamic course of neuron type changes and their underlying molecular mechanisms during the development of neuropathic pain.Boiling is arguably Nature's most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. Central to the boiling process is the development of bubbles. Connecting boiling physics with bubble dynamics is an important, yet daunting challenge because of the intrinsically complex and high dimensional of bubble dynamics. Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology.While there has been a rapid growth of digital health apps to support chronic diseases, clear standards on how to best evaluate the quality of these evolving tools are absent. This scoping review aims to synthesize the emerging field of mobile health app quality assessment by reviewing criteria used by previous studies to assess the quality of mobile apps for chronic disease management. A literature review was conducted in September 2017 for published studies that use a set of quality criteria to directly evaluate two or more patient-facing apps supporting promote chronic disease management. This resulted in 8182 citations which were reviewed by research team members, resulting in 65 articles for inclusion. An inductive coding schema to synthesize the quality criteria utilized by included articles was developed, with 40 unique quality criteria identified. Of the 43 (66%) articles that reported resources used to support criteria selection, 19 (29%) used clinical guidelines, and 10 (15%) used behavior change theory. Deferoxamine purchase The most commonly used criteria included the presence of user engagement or behavior change functions (97%, n = 63) and technical features of the app such as customizability (20%, n = 13, while Usability was assessed by 24 studies (36.9%). This study highlights the significant variation in quality criteria employed for the assessment of mobile health apps. Future methods for app evaluation will benefit from approaches that leverage the best evidence regarding the clinical impact and behavior change mechanisms while more directly reflecting patient needs when evaluating the quality of apps.Recruitment of microorganisms to the rhizosphere varies among plant genotypes, yet an understanding of whether the microbiome can be altered by selection on the host is relatively unknown. Here, we performed a common garden study to characterize recruitment of rhizosphere microbiome, functional groups, for 20 expired Plant Variety Protection Act maize lines spanning a chronosequence of development from 1949 to 1986. This time frame brackets a series of agronomic innovations, namely improvements in breeding and the application of synthetic nitrogenous fertilizers, technologies that define modern industrial agriculture. We assessed the impact of chronological agronomic improvements on recruitment of the rhizosphere microbiome in maize, with emphasis on nitrogen cycling functional groups. In addition, we quantified the microbial genes involved in nitrogen cycling and predicted functional pathways present in the microbiome of each genotype. Both genetic relatednesses of host plant and decade of germplasm development were significant factors in the recruitment of the rhizosphere microbiome. More recently developed germplasm recruited fewer microbial taxa with the genetic capability for sustainable nitrogen provisioning and larger populations of microorganisms that contribute to N losses. This study indicates that the development of high-yielding varieties and agronomic management approaches of industrial agriculture inadvertently modified interactions between maize and its microbiome.