Development of wheat ( L) grain mainly depends on the processes of starch synthesis and storage protein accumulation, which are critical for grain yield and quality. However, the regulatory network underlying the transcriptional and physiological changes of grain development is still not clear. Here, we combined ATAC-seq and RNA-seq to discover the chromatin accessibility and gene expression dynamics during these processes. We found that the chromatin accessibility changes are tightly associated with differential transcriptomic expressions, and the proportion of distal ACRs was increased gradually during grain development. Specific transcription factor (TF) binding sites were enriched at different stages and were diversified among the 3 subgenomes. We further predicted the potential interactions between key TFs and genes related with starch and storage protein biosynthesis and found different copies of some key TFs played diversified roles. Overall, our findings have provided numerous resources and illustrated the regulatory network during wheat grain development, which would shed light on the improvement of wheat yields and qualities. <<<

Chao Xie, Shitong Xiang, Chun Shen, Xuerui Peng, Jujiao Kang, Yuzhu Li, Wei Cheng, Shiqi He, Marina Bobou, M John Broulidakis, Betteke Maria van Noort, Zuo Zhang, Lauren Robinson, Nilakshi Vaidya, Jeanne Winterer, Yuning Zhang, Sinead King, Tobias Banaschewski, Gareth J Barker, Arun L W Bokde, Uli Bromberg, Christian Büchel, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Bernd Ittermann, Hervé Lemaître, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Frauke Nees, Dimitri Papadopoulos Orfanos, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Ulrike Schmidt, Julia Sinclair, Michael N Smolka, Argyris Stringaris, Henrik Walter, Robert Whelan, Sylvane Desrivières, Barbara J Sahakian, Trevor W Robbins, Gunter Schumann, Tianye Jia, Jianfeng Feng, IMAGEN Consortium, STRATIFY/ESTRA Consortium, ZIB Consortium <<<

Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging cohort from adolescence to young adulthood (IMAGEN) to define a neuropsychopathological (NP) factor across externalizing and internalizing symptoms using multitask connectomes. We demonstrate that this NP factor might represent a unified, genetically determined, delayed development of the prefrontal cortex that further leads to poor executive function. We also show this NP factor to be reproducible in multiple developmental periods, from preadolescence to early adulthood, and generalizable to the resting-state connectome and clinical samples (the ADHD-200 Sample and the Stratify Project). In conclusion, we identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities. <<<

SummaryVariable selection plays an important role in high dimensional statistical modelling which nowadays appears in many areas and is key to various scientific discoveries. For problems of large scale or dimensionality p, accuracy of estimation and computational cost are two top concerns. Recently, Candes and Tao have proposed the Dantzig selector using L1-regularization and showed that it achieves the ideal risk up to a logarithmic factor log(p). Their innovative procedure and remarkable result are challenged when the dimensionality is ultrahigh as the factor log(p) can be large and their uniform uncertainty principle can fail. Motivated by these concerns, we introduce the concept of sure screening and propose a sure screening method that is based on correlation learning, called sure independence screening, to reduce dimensionality from high to a moderate scale that is below the sample size. In a fairly general asymptotic framework, correlation learning is shown to have the sure screening property for even exponentially growing dimensionality. As a methodological extension, iterative sure independence screening is also proposed to enhance its finite sample performance. With dimension reduced accurately from high to below sample size, variable selection can be improved on both speed and accuracy, and can then be accomplished by a well-developed method such as smoothly clipped absolute deviation, the Dantzig selector, lasso or adaptive lasso. The connections between these penalized least squares methods are also elucidated. <<<

Jiaqi Hu, Michael Fang, James R Pike, Pamela L Lutsey, A Richey Sharrett, Lynne E Wagenknecht, Timothy M Hughes, Jesse C Seegmiller, Rebecca F Gottesman, Thomas H Mosley, Josef Coresh, Elizabeth Selvin <<<

AIMS/HYPOTHESIS: The aim of this work was to evaluate whether the association of prediabetes with dementia is explained by the intervening onset of diabetes.METHODS: Among participants of the Atherosclerosis Risk in Communities (ARIC) study we defined baseline prediabetes as HbA1c 39-46 mmol/mol (5.7-6.4%) and subsequent incident diabetes as a self-reported physician diagnosis or use of diabetes medication. Incident dementia was ascertained via active surveillance and adjudicated. We quantified the association of prediabetes with dementia risk before and after accounting for the subsequent development of diabetes among ARIC participants without diabetes at baseline (1990-1992; participants aged 46-70 years). We also evaluated whether age at diabetes diagnosis modified the risk of dementia.RESULTS: Among 11,656 participants without diabetes at baseline, 2330 (20.0%) had prediabetes. Before accounting for incident diabetes, prediabetes was significantly associated with the risk of dementia (HR 1.12 [95% CI 1.01, 1.24]). After accounting for incident diabetes, the association was attenuated and non-significant (HR 1.05 [95% CI 0.94, 1.16]). Earlier age of onset of diabetes had the strongest association with dementia: HR 2.92 (95% CI 2.06, 4.14) for onset before 60 years; HR 1.73 (95% CI 1.47, 2.04) for onset at 60-69 years; and HR 1.23 (95% CI 1.08, 1.40) for onset at 70-79 years.CONCLUSIONS/INTERPRETATION: Prediabetes is associated with dementia risk but this risk is explained by the subsequent development of diabetes. Earlier age of onset of diabetes substantially increases dementia risk. Preventing or delaying progression of prediabetes to diabetes will reduce dementia burden. <<<

Dimension reduction techniques for multivariate time series decompose the observed series into a few useful independent/orthogonal univariate components. We develop a spectral domain method for multivariate second-order stationary time series that linearly transforms the observed series into several groups of lower-dimensional multivariate subseries. These multivariate subseries have non-zero spectral coherence among components within a group but have zero spectral coherence among components across groups. The observed series is expressed as a sum of frequency components whose variances are proportional to the spectral matrices at the respective frequencies. The demixing matrix is then estimated using an eigendecomposition on the sum of the variance matrices of these frequency components and its asymptotic properties are derived. Finally, a consistent test on the cross-spectrum of pairs of components is used to find the desired segmentation into the lower-dimensional subseries. The numerical performance of the proposed method is illustrated through simulation examples and an application to modeling and forecasting wind data is presented. <<<

David Ochoa, Andrew Hercules, Miguel Carmona, Daniel Suveges, Asier Gonzalez-Uriarte, Cinzia Malangone, Alfredo Miranda, Luca Fumis, Denise Carvalho-Silva, Michaela Spitzer, Jarrod Baker, Javier Ferrer, Arwa Raies, Olesya Razuvayevskaya, Adam Faulconbridge, Eirini Petsalaki, Prudence Mutowo, Sandra Machlitt-Northen, Gareth Peat, Elaine McAuley, Chuang Kee Ong, Edward Mountjoy, Maya Ghoussaini, Andrea Pierleoni, Eliseo Papa, Miguel Pignatelli, Gautier Koscielny, Mohd Karim, Jeremy Schwartzentruber, David G Hulcoop, Ian Dunham, Ellen M McDonagh <<<

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive. <<<

Maxime Dhainaut, Samuel A Rose, Guray Akturk, Aleksandra Wroblewska, Sebastian R Nielsen, Eun Sook Park, Mark Buckup, Vladimir Roudko, Luisanna Pia, Robert Sweeney, Jessica Le Berichel, C Matthias Wilk, Anela Bektesevic, Brian H Lee, Nina Bhardwaj, Adeeb H Rahman, Alessia Baccarini, Sacha Gnjatic, Dana Pe'er, Miriam Merad, Brian D Brown <<<

While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying extracellular gene functions, particularly in the tissue context. Here, we developed an approach for spatial functional genomics called Perturb-map. We applied Perturb-map to knock out dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Moreover, we paired Perturb-map and spatial transcriptomics for unbiased analysis of CRISPR-edited tumors. We found that in Tgfbr2 knockout tumors, the tumor microenvironment (TME) was converted to a fibro-mucinous state, and T cells excluded, concomitant with upregulated TGFβ and TGFβ-mediated fibroblast activation, indicating that TGFβ-receptor loss on cancer cells increased TGFβ bioavailability and its immunosuppressive effects on the TME. These studies establish Perturb-map for functional genomics within the tissue at single-cell resolution with spatial architecture preserved and provide insight into how TGFβ responsiveness of cancer cells can affect the TME. <<<

Jessica Roelands, Peter J K Kuppen, Eiman I Ahmed, Raghvendra Mall, Tariq Masoodi, Parul Singh, Gianni Monaco, Christophe Raynaud, Noel F C C de Miranda, Luigi Ferraro, Tatiana C Carneiro-Lobo, Najeeb Syed, Arun Rawat, Amany Awad, Julie Decock, William Mifsud, Lance D Miller, Shimaa Sherif, Mahmoud G Mohamed, Darawan Rinchai, Marc Van den Eynde, Rosalyn W Sayaman, Elad Ziv, Francois Bertucci, Mahir Abdulla Petkar, Stephan Lorenz, Lisa Sara Mathew, Kun Wang, Selvasankar Murugesan, Damien Chaussabel, Alexander L Vahrmeijer, Ena Wang, Anna Ceccarelli, Khalid A Fakhro, Gabriele Zoppoli, Alberto Ballestrero, Rob A E M Tollenaar, Francesco M Marincola, Jérôme Galon, Souhaila Al Khodor, Michele Ceccarelli, Wouter Hendrickx, Davide Bedognetti <<<

The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches. <<<

Asian hair is known for its straightness, dark pigmentation, and large diameter. The cuticle layer in Asians is thicker with more compact cuticle cells than that in Caucasians. Asian hair generally exhibits the strongest mechanical properties, and its cross-sectional area is determined greatly by genetic variations, particularly from the gene. However, knowledge on Asian hair remains unclear with limited studies. This article aimed to review and summarize the characteristics and properties of Asian hair. It also aimed to discuss hair disorders including linear lupus panniculitis and pseudocyst of the scalp that occur distinctively in Asian populations. <<<

Mingkun Lu , Jiayi Yin , Qi Zhu , Gaole Lin , Minjie Mou , Fuyao Liu , Ziqi Pan , Nanxin You , Xichen Lian , Fengcheng Li , Hongning Zhang , Lingyan Zheng , Wei Zhang , Hanyu Zhang , Zihao Shen , Zhen Gu , Honglin Li , Feng Zhu <<<

Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market. However, investments in a new drug often go unrewarded due to the long and complex process of drug research and development (R&D). With the advancement of experimental technology and computer hardware, artificial intelligence (AI) has recently emerged as a leading tool in analyzing abundant and high-dimensional data. Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D. Driven by big data in biomedicine, AI has led to a revolution in drug R&D, due to its ability to discover new drugs more efficiently and at lower cost. This review begins with a brief overview of common AI models in the field of drug discovery; then, it summarizes and discusses in depth their specific applications in various stages of drug R&D, such as target discovery, drug discovery and design, preclinical research, automated drug synthesis, and influences in the pharmaceutical market. Finally, the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed. <<<

Andrew J Stout, Miles J Arnett, Kristin Chai, Tina Guo, Lishu Liao, Addison B Mirliani, Miriam L Rittenberg, Michelle Shub, Eugene C White, John S K Yuen, Xiaoli Zhang, David L Kaplan <<<

For cultured meat to succeed at scale, muscle cells from food-relevant species must be expanded in vitro in a rapid and reliable manner to produce millions of metric tons of biomass annually. Toward this goal, genetically immortalized cells offer substantial benefits over primary cells, including rapid growth, escape from cellular senescence, and consistent starting cell populations for production. Here, we develop genetically immortalized bovine satellite cells (iBSCs) via constitutive expression of bovine Telomerase reverse transcriptase (TERT) and Cyclin-dependent kinase 4 (CDK4). These cells achieve over 120 doublings at the time of publication and maintain their capacity for myogenic differentiation. They therefore offer a valuable tool to the field, enabling further research and development to advance cultured meat. <<<

The regularities of the world render an intricate interplay between past and present. Even across independent trials, current-trial perception can be automatically shifted by preceding trials, namely the "serial bias." Meanwhile, the neural implementation of the spontaneous shift of present by past that operates on multiple features remains unknown. In two auditory categorization experiments with human electrophysiological recordings, we demonstrate that serial bias arises from the co-occurrence of past-trial neural reactivation and the neural encoding of current-trial features. The meeting of past and present shifts the neural representation of current-trial features and modulates serial bias behavior. Critically, past-trial features (i.e., pitch, category choice, motor response) keep their respective identities in memory and are only reactivated by the corresponding features in the current trial, giving rise to dissociated feature-specific serial biases. The feature-specific automatic reactivation might constitute a fundamental mechanism for adaptive past-to-present generalizations over multiple features. <<<

DNA methylation is a major epigenetic modification found across species and has a profound impact on many biological processes. However, its influence on chromatin accessibility and higher-order genome organization remains unclear, particularly in plants. Here, we present genome-wide chromatin accessibility profiles of 18 mutants that are deficient in CG, CHG, or CHH DNA methylation. We find that DNA methylation in all three sequence contexts impacts chromatin accessibility in heterochromatin. Many chromatin regions maintain inaccessibility when DNA methylation is lost in only one or two sequence contexts, and signatures of accessibility are particularly affected when DNA methylation is reduced in all contexts, suggesting an interplay between different types of DNA methylation. In addition, we found that increased chromatin accessibility was not always accompanied by increased transcription, suggesting that DNA methylation can directly impact chromatin structure by other mechanisms. We also observed that an increase in chromatin accessibility was accompanied by enhanced long-range chromatin interactions. Together, these results provide a valuable resource for chromatin architecture and DNA methylation analyses and uncover a pivotal role for methylation in the maintenance of heterochromatin inaccessibility. <<<

BACKGROUND: This study was designed to investigate the clinical application, efficacy, and safety of immune checkpoint inhibitors (ICIs) in the treatment of lung cancer in the real world.METHODS: A retrospective, observational analysis was conducted on patients treated with ICIs in four tertiary hospitals in the region from January 2015 to March 2021, to evaluate the clinical efficacy of ICIs single-agent or combined chemotherapy and anti-vascular drugs in the first-line or second-line treatment of patients with lung cancer.RESULTS: Three hundred and fifteen patients were enrolled in this study. In patients with stage III-IV adenocarcinoma and Squamous cell carcinoma, the objective response rate (ORR) and disease control rate (DCR) were 35.5% (87/245) and 93.5% (229/245), respectively, the median progression-free survival (PFS) was 10.8 months, and the median overall survival (OS) was not reached. A total of 132 patients received ICIs as the first-line treatment, the median treatment cycle was 8 cycles (2-20 cycles), the short-term efficacy ORR was 38.6%, DCR was 93.9%, and the median PFS was 11.4 months. One hundred thirteen patients received ICIs treatment as second-line treatment, the median treatment cycle was five cycles (2-10 cycles), the short-term efficacy ORR was 31.9%, DCR was 92.9%, and the median PFS was 10.0 months. There were no statistically significant differences in ORR, DCR, or median PFS with ICIs as the first-line treatment compared with the second-line treatment(P > 0.05). The results of subgroup analysis showed that Eastern Cooperative Oncology Group performance status (ECOG PS), epidermal growth factor receptor (EGFR) mutation status, pathological type and number of treatment lines were not correlated with median PFS(P > 0.05). However, there were statistically significant differences in programmed death-ligand 1(PD-L1) expression, corticosteroid interference, and antibiotic (Abx) treatment among all groups (P < 0.05). In terms of safety, the overall incidence of adverse reactions in 315 patients was 62.5%, and the incidence of immune-related adverse events (irAEs) was 13.7%. Grade 1-2 and 3-4 incidence of adverse events were 34.9 and 27.65%, respectively. There were four patients who experienced fatal irAEs, two cases were liver damage leading to liver failure, one case was immune related pneumonia, and one case was immune related myocarditis.CONCLUSION: In the real world, ICIs has a good effect on patients with lung cancer and significantly improves ORR and PFS. <<<

In addition to the shared weights of the synaptic connections, we proposed a new neural network that includes the synaptic effective range weights for both the forward and back propagation. And lots of simulations were used which RNN cannot be achieved. The simulations of PNN fit very well in experiments and hypotheses of 6 papers CNS Journals, 6 papers of CNS family Journals and 1 paper top Physics Journal [14-26]. The brain plasticity in positive or negative memory may be quantum and produce short-term memory, and exhibits an exponential decay in the wave function over a period of time, produced in the hippocampus. And exponential decay occurs due to barriers, and barriers can refer to astrocytes. Brain plasticity in working memory flows through the brain, from the hippocampus to the cortex, through directional derivatives. The strong working memory brain plasticity turns to long-term memory means maximum of directional derivatives, and maximum of directional derivatives is gradient. Thus, long-term memory signifies the gradient of brain plasticity in working memory. The process of short-term memory turns to long-term memory is the process of non-classically turns to classically. Astrocytic cortex memory persistence factor also inhibits local synaptic accumulation, and the model inspires experiments. This could be the process of astrocytes phagocytose synapses is driven by both positive and negative memories of plasticity in the brain. In simulation, it is possible that thicker cortices and more diverse individuals within the brain could have high IQ, but thickest cortices and most diverse individuals may have low IQ in simulation. PSO considers global solution or best previous solution, but also considers relatively good and relatively inferior solution. And PNN modified ResNet to consider memory gradient. The simple PNN only considers astrocytes phagocytosed synapses. <<<

Despite the success of Transformer models in vision and language tasks, they often learn knowledge from enormous data implicitly and cannot utilize structured input data directly. On the other hand, structured learning approaches such as graph neural networks (GNNs) that integrate prior information can barely compete with Transformer models. In this work, we aim to benefit from both worlds and propose a novel Multimodal Graph Transformer for question answering tasks that requires performing reasoning across multiple modalities. We introduce a graph-involved plug-and-play quasi-attention mechanism to incorporate multimodal graph information, acquired from text and visual data, to the vanilla self-attention as effective prior. In particular, we construct the text graph, dense region graph, and semantic graph to generate adjacency matrices, and then compose them with input vision and language features to perform downstream reasoning. Such a way of regularizing self-attention with graph information significantly improves the inferring ability and helps align features from different modalities. We validate the effectiveness of Multimodal Graph Transformer over its Transformer baselines on GQA, VQAv2, and MultiModalQA datasets. <<<

Background: Qihuang needle therapy is a newly developed acupuncture therapy to treat tic disorders in clinical practice. However, the mechanism to reduce tic severity remains unknown. Changes in intestinal flora and circulation metabolites are perhaps the potential pathogenesis of tic disorders. As a result, we present a protocol for a controlled clinical trial using multi-omics analysis to probe the mechanism of the Qihuang needle in managing tic disorders.Methods: This is a matched-pairs design, controlled, clinical trial for patients with tic disorders. Participants will be allocated to either an experimental group or a healthy control group. The main acupoints are Baihui (GV20), Yintang (EX-HN3), and Jueyinshu (BL14). The experimental group will receive Qihuang needle therapy for a month, while the control group will receive no interventions.Expected outcomes: The change in the severity of the tic disorder is set as the main outcome. Secondary outcomes include gastrointestinal severity index and recurrence rate, which will be calculated after a 12-week follow-up. Gut microbiota, measured by 16S rRNA gene sequencing; serum metabolomics, assessed via LC/MS; and serum zonulin, assessed by enzyme-linked immunosorbent assay (ELISA), will be used as biological specimen analysis outcomes. The present study will investigate the possible interactions between intestinal flora and serum metabolites and the improvement of clinical profiles, which may elucidate the mechanism of Qihuang needle therapy for tic disorders.Trial registration: This trial is registered at the Chinese Clinical Trial Registry (http://www.chictr.org.cn/). Registration number: ChiCTR2200057723, Date: 2022-04-14. <<<

Yazan N Billeh, Binghuang Cai, Sergey L Gratiy, Kael Dai, Ramakrishnan Iyer, Nathan W Gouwens, Reza Abbasi-Asl, Xiaoxuan Jia, Joshua H Siegle, Shawn R Olsen, Christof Koch, Stefan Mihalas, Anton Arkhipov <<<

Structural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, biologically realistic simulation of the awake mouse primary visual cortex. The model was constructed at two levels of granularity, using either biophysically detailed or point neurons. Both variants have identical network connectivity and were compared to each other and to experimental recordings of visual-driven neural activity. While tuning these networks to recapitulate experimental data, we identified rules governing cell-class-specific connectivity and synaptic strengths. These structural constraints constitute hypotheses that can be tested experimentally. Despite their distinct single-cell abstraction, both spatially extended and point models perform similarly at the level of firing rate distributions for the questions we investigated. All data and models are freely available as a resource for the community. <<<

Ann H Partridge, Samuel M Niman, Monica Ruggeri, Fedro A Peccatori, Hatem A Azim, Marco Colleoni, Cristina Saura, Chikako Shimizu, Anna B Sætersdal, Judith R Kroep, Audrey Mailliez, Ellen Warner, Virginia F Borges, Frédéric Amant, Andrea Gombos, Akemi Kataoka, Christine Rousset-Jablonski, Simona Borstnar, Junko Takei, Jeong E Lee, Janice M Walshe, Manuel Ruíz-Borrego, Halle C F Moore, Christobel Saunders, Vesna Bjelic-Radisic, Snezana Susnjar, Fatima Cardoso, Karen L Smith, Teresa Ferreiro, Karin Ribi, Kathryn Ruddy, Roswitha Kammler, Sarra El-Abed, Giuseppe Viale, Martine Piccart, Larissa A Korde, Aron Goldhirsch, Richard D Gelber, Olivia Pagani, International Breast Cancer Study Group, POSITIVE Trial Collaborators <<<

BACKGROUND: Prospective data on the risk of recurrence among women with hormone receptor-positive early breast cancer who temporarily discontinue endocrine therapy to attempt pregnancy are lacking.METHODS: We conducted a single-group trial in which we evaluated the temporary interruption of adjuvant endocrine therapy to attempt pregnancy in young women with previous breast cancer. Eligible women were 42 years of age or younger; had had stage I, II, or III disease; had received adjuvant endocrine therapy for 18 to 30 months; and desired pregnancy. The primary end point was the number of breast cancer events (defined as local, regional, or distant recurrence of invasive breast cancer or new contralateral invasive breast cancer) during follow-up. The primary analysis was planned to be performed after 1600 patient-years of follow-up. The prespecified safety threshold was the occurrence of 46 breast cancer events during this period. Breast cancer outcomes in this treatment-interruption group were compared with those in an external control cohort consisting of women who would have met the entry criteria for the current trial.RESULTS: Among 516 women, the median age was 37 years, the median time from breast cancer diagnosis to enrollment was 29 months, and 93.4% had stage I or II disease. Among 497 women who were followed for pregnancy status, 368 (74.0%) had at least one pregnancy and 317 (63.8%) had at least one live birth. In total, 365 babies were born. At 1638 patient-years of follow-up (median follow-up, 41 months), 44 patients had a breast cancer event, a result that did not exceed the safety threshold. The 3-year incidence of breast cancer events was 8.9% (95% confidence interval [CI], 6.3 to 11.6) in the treatment-interruption group and 9.2% (95% CI, 7.6 to 10.8) in the control cohort.CONCLUSIONS: Among select women with previous hormone receptor-positive early breast cancer, temporary interruption of endocrine therapy to attempt pregnancy did not confer a greater short-term risk of breast cancer events, including distant recurrence, than that in the external control cohort. Further follow-up is critical to inform longer-term safety. (Funded by ETOP IBCSG Partners Foundation and others; POSITIVE ClinicalTrials.gov number, NCT02308085.). <<<

Decision-making requires flexibility to rapidly switch one's actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse's choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network. <<<