ABI4 Mediates Antagonistic Effects of Abscisic Acid and Gibberellins at Transcript and Protein Levels

Abscisic acid (ABA) and gibberellins (GA) are plant hormones which antagonistically mediate numerous physiological processes, and their optimal balance is essential for normal plant development. However, the molecular mechanism underlying ABA and GA antagonism still needs to be determined. Here, we report that ABA- INSENSITIVE 4 (ABI4) is a central factor for GA/ABA homeostasis and antagonism in post-germination stages. ABI4 over-expression in Arabidopsis (OE-ABI4) leads to developmental defects including a decrease in plant height and poor seed production. The transcription of a key ABA biosynthetic gene, NCED6, and of a key GA catabolic gene, GA2ox7, is significantly enhanced by ABI4 over-expression. ABI4 activates NCED6 and GA2ox7 transcription by directly binding to the promoters, and genetic analysis revealed that mutation in these two genes partially rescues the dwarf phenotype of ABI4 overexpressing plants. Consistently, ABI4 overexpressing seedlings have a lower GA/ABA ratio compared to the wild type. We further show that ABA induces GA2ox7 transcription while GA represses NCED6 expression in an ABI4-dependent manner; and that ABA stabilizes the ABI4 protein, whereas GA promotes its degradation. Taken together, these results propose that ABA and GA antagonize each other by oppositely acting on ABI4 transcript and protein levels

A reinforcement learning agent for head and neck intensity-modulated radiation therapy

Head and neck (HN) cancers pose a difficult problem in the planning of intensity-modulated radiation therapy (IMRT) treatment. The primary tumor can be large and asymmetrical, and multiple organs at risk (OARs) with varying dose-sparing goals lie close to the target volume. Currently, there is no systematic way of automating the generation of IMRT plans, and the manual options face planning quality and long planning time challenges. In this article, we present a reinforcement learning (RL) model for the purposes of providing automated treatment planning to reduce clinical workflow time as well as providing a better starting point for human planners to modify and build upon. Several models with progressing complexity are presented, including the relevant plan dosimetry analysis and model interpretations of the resulting strategies learned by the auto-planning agent. Models were trained on a set of 40 patients and validated on a set of 20 patients. The presented models are shown to be consistent with the requirements of an RL model to be underpinned by a Markov decision process (MDP). In-depth interpretability of the models is presented by examination of the decision space using action hyperplanes. The auto-planning agent was able to generate plans with superior reduction in the mean dose of the left and right parotid glands by approximately 7 Gy ± 2.5 Gy (p < 0.01) over a starting, static template plan with only pre-defined general prescription information. RL plans were comparable to a human expert’s clinical plans for the primary (44 Gy), boost (26 Gy) , and the summed plans (70 Gy) with p-values of 0.43, 0.72, and 0.67, respectively, for the dosimetric endpoints and uniform target coverage normalization. The RL planning agent was able to produce the plans used in validation in an average of 13.58 min, with a minimum and a maximum planning time of 2.27 and 44.82 min, respectively

Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: An intertechnique and interinstitutional study

Purpose: To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution

Insights into salt tolerance from the genome of Thellungiella salsuginea

Thellungiella salsuginea, a close relative of Arabidopsis, represents an extremophile model for abiotic stress tolerance studies. We present the draft sequence of the T. salsuginea genome, assembled based on ∼134-fold coverage to seven chromosomes with a coding capacity of at least 28,457 genes. This genome provides resources and evidence about the nature of defense mechanisms constituting the genetic basis underlying plant abiotic stress tolerance. Comparative genomics and experimental analyses identified genes related to cation transport, abscisic acid signaling, and wax production prominent in T. salsuginea as possible contributors to its success in stressful environments

An Ensemble Approach to Knowledge-Based Intensity-Modulated Radiation Therapy Planning

Knowledge-based planning (KBP) utilizes experienced planners’ knowledge embedded in prior plans to estimate optimal achievable dose volume histogram (DVH) of new cases. In the regression-based KBP framework, previously planned patients’ anatomical features and DVHs are extracted, and prior knowledge is summarized as the regression coefficients that transform features to organ-at-risk DVH predictions. In our study, we find that in different settings, different regression methods work better. To improve the robustness of KBP models, we propose an ensemble method that combines the strengths of various linear regression models, including stepwise, lasso, elastic net, and ridge regression. In the ensemble approach, we first obtain individual model prediction metadata using in-training-set leave-one-out cross validation. A constrained optimization is subsequently performed to decide individual model weights. The metadata is also used to filter out impactful training set outliers. We evaluate our method on a fresh set of retrospectively retrieved anonymized prostate intensity-modulated radiation therapy (IMRT) cases and head and neck IMRT cases. The proposed approach is more robust against small training set size, wrongly labeled cases, and dosimetric inferior plans, compared with other individual models. In summary, we believe the improved robustness makes the proposed method more suitable for clinical settings than individual models

Room-temperature Single-molecule Conductance Switch via Confined Coordination-induced Spin-State Manipulation

The emerging of molecular spintronics offers a unique chance for the design of molecular devices with different spin-state, and the control of spin-state becomes essential for molecular spin switches. However, the intrinsic spin switching from low-spin to high-spin state is a temperature-dependent process with a small energy barrier that low temperature is required to maintain the low-spin state, and thus the room-temperature operation of single-molecule devices have not yet been achieved. Here, we present a reversible single-molecule conductance switch by manipulating the spin states of the molecule at room temperature using the scanning tunneling microscope break-junction (STM-BJ) technique. The manipulation of the spin states between S = 0 and S = 1 is achieved by complexing or decomplexing the pyridine derivative molecule with the square planar nickel(II) porphyrin. The bias-dependent conductance evolution proved that the strong electric field between the nanoelectrodes plays a crucial role in the coordination reaction. The DFT calculations further revealed that the conductance changes come from the geometry change of the porphyrin ring and spin-state switching of Ni(II) ion. Our work provides a new avenue to investigate room-temperature spin-related sensors and molecular spintronics