NEK1 Phosphorylation of YAP Promotes Its Stabilization and Transcriptional Output

Most prostate cancer (PCa) deaths result from progressive failure in standard androgen deprivation therapy (ADT), leading to metastatic castration-resistant PCa (mCRPC); however, the mechanism and key players leading to this are not fully understood. While studying the role of tousled-like kinase 1 (TLK1) and never in mitosis gene A (NIMA)-related kinase 1 (NEK1) in a DNA damage response (DDR)-mediated cell cycle arrest in LNCaP cells treated with bicalutamide, we uncovered that overexpression of wt-NEK1 resulted in a rapid conversion to androgen-independent (AI) growth, analogous to what has been observed when YAP1 is overexpressed. We now report that overexpression of wt-NEK1 results in accumulation of YAP1, suggesting the existence of a TLK1\u3eNEK1\u3eYAP1 axis that leads to adaptation to AI growth. Further, YAP1 is co-immunoprecipitated with NEK1. Importantly, NEK1 was able to phosphorylate YAP1 on six residues in vitro, which we believe are important for stabilization of the protein, possibly by increasing its interaction with transcriptional partners. In fact, knockout (KO) of NEK1 in NT1 PCa cells resulted in a parallel decrease of YAP1 level and reduced expression of typical YAP-regulated target genes. In terms of cancer potential implications, the expression of NEK1 and YAP1 proteins was found to be increased and correlated in several cancers. These include PCa stages according to Gleason score, head and neck squamous cell carcinoma, and glioblastoma, suggesting that this co-regulation is imparted by increased YAP1 stability when NEK1 is overexpressed or activated by TLK1, and not through transcriptional co-expression. We propose that the TLK1\u3eNEK1\u3eYAP1 axis is a key determinant for cancer progression, particularly during the process of androgen-sensitive to -independent conversion during progression to mCRPC

Sensitivity analysis and automation for intraoperative implementation of the atlas-based method for brain shift correction

ABSTRACT The use of biomechanical models to correct the misregistration due to deformation in image guided neurosurgical systems has been a growing area of investigation. In previous work, an atlas-based inverse model was developed to account for soft-tissue deformations during image-guided surgery. Central to that methodology is a considerable amount of pre-computation and planning. The goal of this work is to evaluate techniques that could potentially reduce that burden. Distinct from previous manual techniques, an automated segmentation technique is described for the cerebrum and dural septa. The shift correction results using this automated segmentation method were compared to those using the manual methods. In addition, the extent and distribution of the surgical parameters associated with the deformation atlas were investigated by a sensitivity analysis using simulation experiments and clinical data. The shift correction results did not change significantly using the automated method (correction of 73±13% ) as compared to the semi-automated method from previous work (correction of 76±13%). The results of the sensitivity analysis show that the atlas could be constructed by coarser sampling (six fold reduction) without substantial degradation in the shift reconstruction, a decrease in preoperative computational time from 13.1±3.5 hours to 2.2±0.6 hours. The automated segmentation technique and the findings of the sensitivity study have significant impact on the reduction of pre-operative computational time, improving the utility of the atlas-based method. The work in this paper suggests that the atlas-based technique can become a 'time of surgery' setup procedure rather than a pre-operative computing strategy

Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior

Shannon, in his seminal paper introducing information theory, divided the communication into three levels: technical, semantic, and effectivenss. While the technical level is concerned with accurate reconstruction of transmitted symbols, the semantic and effectiveness levels deal with the inferred meaning and its effect on the receiver. Thanks to telecommunications, the first level problem has produced great advances like the internet. Large Language Models (LLMs) make some progress towards the second goal, but the third level still remains largely untouched. The third problem deals with predicting and optimizing communication for desired receiver behavior. LLMs, while showing wide generalization capabilities across a wide range of tasks, are unable to solve for this. One reason for the underperformance could be a lack of "behavior tokens" in LLMs' training corpora. Behavior tokens define receiver behavior over a communication, such as shares, likes, clicks, purchases, retweets, etc. While preprocessing data for LLM training, behavior tokens are often removed from the corpora as noise. Therefore, in this paper, we make some initial progress towards reintroducing behavior tokens in LLM training. The trained models, other than showing similar performance to LLMs on content understanding tasks, show generalization capabilities on behavior simulation, content simulation, behavior understanding, and behavior domain adaptation. Using a wide range of tasks on two corpora, we show results on all these capabilities. We call these models Large Content and Behavior Models (LCBMs). Further, to spur more research on LCBMs, we release our new Content Behavior Corpus (CBC), a repository containing communicator, message, and corresponding receiver behavior

Childhood tonsillectomy alters the primary distribution of HPV‐related oropharyngeal squamous cell carcinoma

ObjectivesWe investigated how tonsillectomy during childhood may influence the distribution of human papillomavirus (HPV) positive cancer of the tonsils in adult life using p16 as a surrogate marker for HPV infection.Study DesignRetrospective observational study.MethodsA total of 280 patients diagnosed with oropharyngeal squamous cell carcinoma (OPSCC) and known p16 status were eligible for this study. Each participant was called to obtain the childhood tonsillectomy history. Respondents were subgrouped by p16 status and the primary tumor location. Patient demographic and clinical information was analyzed for association with Fisher’s exact and Wilcoxon rank sum tests. Location of tumor was modeled using univariate (UVA) and multivariate (MVA) logistic regression with associated odds ratios (OR) and 95% confidence intervals.ResultsOf the 280 patients, 115 (41%) were respondents: 104 (90.4%) were p16 positive and 11 (9.6%) were p16 negative. For p16 positive patients, we observed a majority (93%) of intact tonsils in those with tonsil cancer, compared to 45% of intact tonsils in patients with p16 positive cancer elsewhere in the oropharynx (P < .001). MVA logistic regression showed that female gender (OR = 4.16, P = .0675), prior smoking history (OR = 2.6, P = .0367), and intact tonsils (OR = 15.2, P < .0001) were associated with tonsillar OPSCC.ConclusionWe found that patients with p16 positive OPSCC at a non‐tonsil site were much more likely to have had prior tonsillectomy vs those with p16 positive OPSCC arising within the tonsil. Nevertheless, we do not advocate tonsillectomies as a public health policy to reduce HPV‐related OPSCC.Level of Evidence6Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154902/1/lio2342_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154902/2/lio2342.pd

Tell me why! Explanations support learning relational and causal structure

Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of explanations--plays a considerable role in overcoming this challenge. Here, we show that language can play a similar role for deep RL agents in complex environments. While agents typically struggle to acquire relational and causal knowledge, augmenting their experience by training them to predict language descriptions and explanations can overcome these limitations. We show that language can help agents learn challenging relational tasks, and examine which aspects of language contribute to its benefits. We then show that explanations can help agents to infer not only relational but also causal structure. Language can shape the way that agents to generalize out-of-distribution from ambiguous, causally-confounded training, and explanations even allow agents to learn to perform experimental interventions to identify causal relationships. Our results suggest that language description and explanation may be powerful tools for improving agent learning and generalization.Comment: ICML 2022; 23 page

Bioactive glycans in a microbiome-directed food for children with malnutrition

Evidence is accumulating that perturbed postnatal development of the gut microbiome contributes to childhood malnutritio

Hexanary blends: a strategy towards thermally stable organic photovoltaics

Non-fullerene based organic solar cells display a high initial power conversion efficiency but continue to suffer from poor thermal stability, especially in case of devices with thick active layers. Mixing of five structurally similar acceptors with similar electron affinities, and blending with a donor polymer is explored, yielding devices with a power conversion efficiency of up to 17.6%. The hexanary device performance is unaffected by thermal annealing of the bulk-heterojunction active layer for at least 23 days at 130 \ub0C in the dark and an inert atmosphere. Moreover, hexanary blends offer a high degree of thermal stability for an active layer thickness of up to 390 nm, which is advantageous for high-throughput processing of organic solar cells. Here, a generic strategy based on multi-component acceptor mixtures is presented that permits to considerably improve the thermal stability of non-fullerene based devices and thus paves the way for large-area organic solar cells

A CDC20-APC/SOX2 Signaling Axis Regulates Human Glioblastoma Stem-like Cells

SummaryGlioblastoma harbors a dynamic subpopulation of glioblastoma stem-like cells (GSCs) that can propagate tumors in vivo and is resistant to standard chemoradiation. Identification of the cell-intrinsic mechanisms governing this clinically important cell state may lead to the discovery of therapeutic strategies for this challenging malignancy. Here, we demonstrate that the mitotic E3 ubiquitin ligase CDC20-anaphase-promoting complex (CDC20-APC) drives invasiveness and self-renewal in patient tumor-derived GSCs. Moreover, CDC20 knockdown inhibited and CDC20 overexpression increased the ability of human GSCs to generate brain tumors in an orthotopic xenograft model in vivo. CDC20-APC control of GSC invasion and self-renewal operates through pluripotency-related transcription factor SOX2. Our results identify a CDC20-APC/SOX2 signaling axis that controls key biological properties of GSCs, with implications for CDC20-APC-targeted strategies in the treatment of glioblastoma