Development: A Pathway to Plant Female Germ Cells

SummaryPlant germ cells form late in development, but little is known about the molecular basis for germline specification in plants. Recent results have identified components of a regulatory pathway controlling female germ cell determination, including a key transcription factor and some putative signaling proteins

Asteroseismic Modeling of 1,153 Kepler Red Giant Branch Stars: Improved Stellar Parameters with Gravity-Mode Period Spacings and Luminosity Constraints

This paper reports estimated stellar parameters of 1,153 Kepler red giant branch stars determined with asteroseismic modeling. We use radial-mode oscillation frequencies, gravity-mode period spacings, Gaia luminosities, and spectroscopic data to characterize these stars. Compared with previous studies, we find that the two additional observed constraints, i.e., the gravity-mode period spacing and luminosity, significantly improve the precision of fundamental stellar parameters. The typical uncertainties are 2.9% for the mass, 11% for the age, 1.0% for the radius, 0.0039 dex for the surface gravity, and 0.5\% for the helium core mass, making this the best-characterized large sample of red-giant stars available to date. With better characterizations for these red giants, we recalibrate the seismic scaling relations and study the surface term on the red-giant branch. We confirm that the surface term depends on the surface gravity and effective temperature, but there is no significant correlation with metallicity.Comment: Accepted by Ap

Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources

Background: Data sharing has been a big challenge in biomedical informatics because of privacy concerns. Contextual embedding models have demonstrated a very strong representative capability to describe medical concepts (and their context), and they have shown promise as an alternative way to support deep-learning applications without the need to disclose original data. However, contextual embedding models acquired from individual hospitals cannot be directly combined because their embedding spaces are different, and naive pooling renders combined embeddings useless. Objective: The aim of this study was to present a novel approach to address these issues and to promote sharing representation without sharing data. Without sacrificing privacy, we also aimed to build a global model from representations learned from local private data and synchronize information from multiple sources. Methods: We propose a methodology that harmonizes different local contextual embeddings into a global model. We used Word2Vec to generate contextual embeddings from each source and Procrustes to fuse different vector models into one common space by using a list of corresponding pairs as anchor points. We performed prediction analysis with harmonized embeddings. Results: We used sequential medical events extracted from the Medical Information Mart for Intensive Care III database to evaluate the proposed methodology in predicting the next likely diagnosis of a new patient using either structured data or unstructured data. Under different experimental scenarios, we confirmed that the global model built from harmonized local models achieves a more accurate prediction than local models and global models built from naive pooling. Conclusions: Such aggregation of local models using our unique harmonization can serve as the proxy for a global model, combining information from a wide range of institutions and information sources. It allows information unique to a certain hospital to become available to other sites, increasing the fluidity of information flow in health care

Mild traumatic brain injury is associated with effect of inflammation on structural changes of default mode network in those developing chronic pain

BACKGROUND: Mild traumatic brain injury (mTBI) has a higher prevalence (more than 50%) of developing chronic posttraumatic headache (CPTH) compared with moderate or severe TBI. However, the underlying neural mechanism for CPTH remains unclear. This study aimed to investigate the inflammation level and cortical volume changes in patients with acute PTH (APTH) and further examine their potential in identifying patients who finally developed CPTH at follow-up. METHODS: Seventy-seven mTBI patients initially underwent neuropsychological measurements, 9-plex panel of serum cytokines and MRI scans within 7 days post-injury (T-1) and 54 (70.1%) of patients completed the same protocol at a 3-month follow-up (T-2). Forty-two matched healthy controls completed the same protocol at T-1 once. RESULTS: At baseline, mTBI patients with APTH presented significantly increased GM volume mainly in the right dorsal anterior cingulate cortex (dACC) and dorsal posterior cingulate cortex (dPCC), of which the dPCC volume can predict much worse impact of headache on patients\u27 lives by HIT-6 (β = 0.389, P = 0.007) in acute stage. Serum levels of C-C motif chemokine ligand 2 (CCL2) were also elevated in these patients, and its effect on the impact of headache on quality of life was partially mediated by the dPCC volume (mean [SE] indirect effect, 0.088 [0.0462], 95% CI, 0.01-0.164). Longitudinal analysis showed that the dACC and dPCC volumes as well as CCL2 levels had persistently increased in patients developing CPTH 3 months postinjury. CONCLUSION: The findings suggested that structural remodelling of DMN brain regions were involved in the progression from acute to chronic PTH following mTBI, which also mediated the effect of inflammation processes on pain modulation. TRIAL REGISTRATION: ClinicalTrial.gov ID: NCT02868684 ; registered 16 August 2016

Arabidopsis Cell Division Cycle 20.1 Is Required for Normal Meiotic Spindle Assembly and Chromosome Segregation

Cell division requires proper spindle assembly; a surveillance pathway, the spindle assembly checkpoint (SAC), monitors whether the spindle is normal and correctly attached to kinetochores. The SAC proteins regulate mitotic chromosome segregation by affecting CDC20 (Cell Division Cycle 20) function. However, it is unclear whether CDC20 regulates meiotic spindle assembly and proper homolog segregation. Here, we show that the Arabidopsis thaliana CDC20.1 gene is indispensable for meiosis and male fertility. We demonstrate that cdc20.1 meiotic chromosomes align asynchronously and segregate unequally and the metaphase I spindle has aberrant morphology. Comparison of the distribution of meiotic stages at different time points between the wild type and cdc20.1 reveals a delay of meiotic progression from diakinesis to anaphase I. Furthermore, cdc20.1 meiocytes exhibit an abnormal distribution of a histone H3 phosphorylation mark mediated by the Aurora kinase, providing evidence that CDC20.1 regulates Aurora localization for meiotic chromosome segregation. Further evidence that CDC20.1 and Aurora are functionally related was provided by meiosis-specific knockdown of At-Aurora1 expression, resulting in meiotic chromosome segregation defects similar to those of cdc20.1. Taken together, these results suggest a critical role for CDC20.1 in SAC-dependent meiotic chromosome segregation

Editorial: Meiosis in plants: sexual reproduction, genetic variation and crop improvement

Meiosis is essential for sexual reproduction and required for the formation of sperm and egg; its central events are the associations between homologous chromosomes (homologs), including pairing, synapsis, recombination and segregation. During recombination, the exchange of DNA between homologs results in new allelic combinations between the parents and offspring and among individual progeny (Wang and Copenhaver, 2018). This genetic variation is the foundation for biodiversity and speciation. The phenotypic diversity that results from genetic variation is also used to develop new elite traits during commercial plant and animal breeding practices. Thus, understanding the molecular mechanisms drive and regulate plant meiosis can accelerate crop improvement, and provide a theoretical foundation for the development and maintenance of new agricultural varieties

Let Models Speak Ciphers: Multiagent Debate through Embeddings

Discussion and debate among Large Language Models (LLMs) have gained considerable attention due to their potential to enhance the reasoning ability of LLMs. Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary. In this paper, we introduce a communication regime named CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. Specifically, we remove the token sampling step from LLMs and let them communicate their beliefs across the vocabulary through the expectation of the raw transformer output embeddings. Remarkably, by deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights. While the state-of-the-art LLM debate methods using natural language outperforms traditional inference by a margin of 1.5-8%, our experiment results show that CIPHER debate further extends this lead by 1-3.5% across five reasoning tasks and multiple open-source LLMs of varying sizes. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs