BackgroundVariation in seed oil composition and content among soybean varieties is largely attributed to differences in transcript sequences and/or transcript accumulation of oil production related genes in seeds. Discovery and analysis of sequence and expression variations in these genes will accelerate soybean oil quality improvement.ResultsIn an effort to identify these variations, we sequenced the transcriptomes of soybean seeds from nine lines varying in oil composition and/or total oil content. Our results showed that 69,338 distinct transcripts from 32,885 annotated genes were expressed in seeds. A total of 8,037 transcript expression polymorphisms and 50,485 transcript sequence polymorphisms (48,792 SNPs and 1,693 small Indels) were identified among the lines. Effects of the transcript polymorphisms on their encoded protein sequences and functions were predicted. The studies also provided independent evidence that the lack of FAD2-1A gene activity and a non-synonymous SNP in the coding sequence of FAB2C caused elevated oleic acid and stearic acid levels in soybean lines M23 and FAM94-41, respectively.ConclusionsAs a proof-of-concept, we developed an integrated RNA-seq and bioinformatics approach to identify and functionally annotate transcript polymorphisms, and demonstrated its high effectiveness for discovery of genetic and transcript variations that result in altered oil quality traits. The collection of transcript polymorphisms coupled with their predicted functional effects will be a valuable asset for further discovery of genes, gene variants, and functional markers to improve soybean oil quality.

Various algorithms in reinforcement learning exhibit dramatic variability in their convergence rates and ultimate accuracy as a function of the problem structure. Such instance-specific behavior is not captured by existing global minimax bounds, which are worst-case in nature. We analyze the problem of estimating optimal Q-value functions for a discounted Markov decision process with discrete states and actions and identify an instance-dependent functional that controls the difficulty of estimation in the 8 -norm. Using a local minimax framework, we show that this functional arises in lower bounds on the accuracy on any estimation procedure. In the other direction, we establish the sharpness of our lower bounds, up to factors logarithmic in the state and action spaces, by analyzing a variance-reduced version of Q-learning. Our theory provides a precise way of distinguishing "easy" problems from "hard" ones in the context of Q-learning, as illustrated by an ensemble with a continuum of difficulty.

ImportanceAlthough isotretinoin may rarely be associated with laboratory abnormalities such as hypertriglyceridemia, the optimal approach to laboratory monitoring is uncertain, and there is wide variation in clinical practice.ObjectiveTo establish a consensus for isotretinoin laboratory monitoring among a diverse, international cohort of clinical and research experts in acne.Design, Setting, and ParticipantsUsing a modified electronic Delphi process, 4 rounds of anonymous electronic surveys were administered from 2021 to 2022. For laboratory tests reaching consensus (≥70% agreement) for inclusion, questions regarding more time-specific monitoring throughout isotretinoin therapy were asked in subsequent rounds. The participants were international board-certified dermatologist acne experts who were selected on a voluntary basis based on involvement in acne-related professional organizations and research.Main Outcomes and MeasuresThe primary outcome measured was whether participants could reach consensus on key isotretinoin laboratory monitoring parameters.ResultsThe 22 participants from 5 continents had a mean (SD) time in practice of 23.7 (11.6) years and represented a variety of practice settings. Throughout the 4-round study, participation rates ranged from 90% to 100%. Consensus was achieved for the following: check alanine aminotransferase within a month prior to initiation (89.5%) and at peak dose (89.5%) but not monthly (76.2%) or after treatment completion (73.7%); check triglycerides within a month prior to initiation (89.5%) and at peak dose (78.9%) but not monthly (84.2%) or after treatment completion (73.7%); do not check complete blood cell count or basic metabolic panel parameters at any point during isotretinoin treatment (all >70%); do not check gamma-glutamyl transferase (78.9%), bilirubin (81.0%), albumin (72.7%), total protein (72.7%), low-density lipoprotein (73.7%), high-density lipoprotein (73.7%), or C-reactive protein (77.3%).Conclusions and RelevanceThis Delphi study identified a core set of laboratory tests that should be evaluated prior to and during treatment with isotretinoin. These results provide valuable data to guide clinical practice and clinical guideline development to optimize laboratory monitoring in patients treated with isotretinoin.

Melanoma is commonly driven by activating mutations in the MAP kinase BRAF; however, oncogenic BRAF alone is insufficient to promote melanomagenesis. Instead, its expression induces a transient proliferative burst that ultimately ceases with the development of benign nevi comprised of growth-arrested melanocytes. The tumor suppressive mechanisms that restrain nevus melanocyte proliferation remain poorly understood. Here we utilize cell and murine models to demonstrate that oncogenic BRAF leads to activation of the Hippo tumor suppressor pathway, both in melanocytes in vitro and nevus melanocytes in vivo. Mechanistically, we show that oncogenic BRAF promotes both ERK-dependent alterations in the actin cytoskeleton and whole-genome doubling events, which independently reduce RhoA activity to promote Hippo activation. We also demonstrate that functional impairment of the Hippo pathway enables oncogenic BRAF-expressing melanocytes to bypass nevus formation and rapidly form melanomas. Our data reveal that the Hippo pathway enforces the stable arrest of nevus melanocytes and represents a critical barrier to melanoma development.