Understanding and Negotiating Book Publication Contracts

Authors are sometimes so grateful to get a publication offer that they may be tempted to sign the first version of a publication contract that they receive, especially if negotiating seems complicated, intimidating, or risky. But there is a lot at stake for authors in a book deal, and it is well worth the effort to read the contract, understand its contents, and negotiate for favorable terms. A publication contract outlines what rights you are granting to your publisher and on what terms. If you sign away all of the rights in your book to the publisher for the life of copyright, this will preclude you from certain future uses of your work. Even if you don’t have any future plans for your work, you may still regret transferring all rights if your publisher does not make full use of them, your book falls out of print, or sales drop. The terms of the contract can also dictate the specifics of how you will be paid for your book, how much input you have in the appearance of your book, your commitments relating to the manuscript and future works, who is responsible for legal claims stemming from your book’s content, how your book will be promoted, whether and how you can regain control over the rights to your book, and much more. Understanding and Negotiating Book Publication Contracts identifies clauses that frequently appear in publishing contracts, explains in plain language what these terms (and typical variations) mean, and presents strategies for negotiating “author-friendly” versions of these clauses. When authors have more information about copyright and publication options for their works, they are better able to make and keep their works available in the ways they want

Markers for human haematopoietic stem cells : The disconnect between an identification marker and its function

The haematopoietic system is a classical stem cell hierarchy that maintains all the blood cells in the body. Haematopoietic stem cells (HSCs) are rare, highly potent cells that reside at the apex of this hierarchy and are historically some of the most well studied stem cells in humans and laboratory models, with haematopoiesis being the original system to define functional cell types by cell surface markers. Whilst it is possible to isolate HSCs to near purity, we know very little about the functional activity of markers to purify HSCs. This review will focus on the historical efforts to purify HSCs in humans based on cell surface markers, their putative functions and recent advances in finding functional markers on HSCs

Automated detection of bird roosts using NEXRAD radar data and Convolutional neural networks

Although NEXRAD radars have proven to be an effective tool for detecting airborne animals, detecting biological phenomena in radar images often involves a manual, time‐ consuming data‐extraction process. This paper focuses on applying machine learning to automatically find radar data that snapshots large aggregations of birds (specifically Purple Martins and Tree Swallows) as they depart en masse from roosting sites. These aggregations are evident in radar images as rings of elevated reflectivity that appear early in the morning as birds depart from roost sites. Our goal was to develop an algorithm that could determine whether an individual radar image contained at least one Purple Martin or Tree Swallow roost. We use a dataset of known roost locations to train three machine learning algorithms that employed (1) a traditional Artificial Neural Network (ANN), (2) a sophisticated preexisting Convolutional Neural Network (CNN) called Inception‐v3, and (3) a shallow CNN built from scratch. The resulting programs were all effective at finding bird roosts, with both the shallow CNN and the Inception‐v3 network making correct determinations about 90 per cent of the time with an AUC above .9. To the best of our knowledge, this study is the first to apply neural networks in the analysis of bird roosts in radar imagery, and these analytical tools offer new avenues of research into the ecology and behavior of flying animals, with practical applications to wind farm placement, air traffic administration and wildlife conservation. The NEXRAD radar network offers a tremendous archive of continental‐scale data and has the potential to capture entire vertebrate populations. We apply existing machine learning models to a new dataset which constitutes a valuable approach to extracting information from this archive.The funding from the NSF-DGE-1545261 grant helped make this research possible. We thank Sandra Pletschet for her time spent collecting the roost data and Dr. Phillip Chilson for his advice on the project. Some of the computing for this project was performed at the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma (OU). Article processing charges for this publication funded in part by the University of Oklahoma Libraries Open Access Fund.Ye

ZMIZ1 enhances ERα-dependent expression of E2F2 in breast cancer

The estrogen receptor-α (ER) drives 75% of breast cancers. On activation, the ER recruits and assembles a 1–2 MDa transcriptionally active complex. These complexes can modulate tumour growth, and understanding the roles of individual proteins within these complexes can help identify new therapeutic targets. Here, we present the discovery of ER and ZMIZ1 within the same multi-protein assembly by quantitative proteomics, and validated by proximity ligation assay. We characterise ZMIZ1 function by demonstrating a significant decrease in the proliferation of ER-positive cancer cell lines. To establish a role for the ER-ZMIZ1 interaction, we measured the transcriptional changes in the estrogen response post-ZMIZ1 knockdown using an RNA-seq time-course over 24 h. Gene set enrichment analysis of the ZMIZ1-knockdown data identified a specific delay in the response of estradiol-induced cell cycle genes. Integration of ENCODE data with our RNA-seq results identified that ER and ZMIZ1 both bind the promoter of E2F2. We therefore propose that ER and ZMIZ1 interact to enable the efficient estrogenic response at subset of cell cycle genes via a novel ZMIZ1–ER–E2F2 signalling axis. Finally, we show that high ZMIZ1 expression is predictive of worse patient outcome, ER and ZMIZ1 are co-expressed in breast cancer patients in TCGA and METABRIC, and the proteins are co-localised within the nuclei of tumour cell in patient biopsies. In conclusion, we establish that ZMIZ1 is a regulator of the estrogenic cell cycle response and provide evidence of the biological importance of the ER–ZMIZ1 interaction in ER-positive patient tumours, supporting potential clinical relevance

S6K2-mediated regulation of TRBP as a determinant of miRNA expression in human primary lymphatic endothelial cells

MicroRNAs (miRNAs) are short non-coding RNAs that silence mRNAs. They are generated following transcription and cleavage by the DROSHA/DGCR8 and DICER/TRBP/PACT complexes. Although it is known that components of the miRNA biogenesis machinery can be phosphorylated, it remains poorly understood how these events become engaged during physiological cellular activation. We demonstrate that S6 kinases can phosphorylate the extended C-terminal domain of TRBP and interact with TRBP in situ in primary cells. TRBP serines 283/286 are essential for S6K-mediated TRBP phosphorylation, optimal expression of TRBP, and the S6K-TRBP interaction in human primary cells. We demonstrate the functional relevance of this interaction in primary human dermal lymphatic endothelial cells (HDLECs). Angiopoietin-1 (ANG1) can augment miRNA biogenesis in HDLECs through enhancing TRBP phosphorylation and expression in an S6K2-dependent manner. We propose that the S6K2/TRBP node controls miRNA biogenesis in HDLECs and provides a molecular link between the mTOR pathway and the miRNA biogenesis machinery

Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks

The amounts of muscle and fat in a person's body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold standard for measuring body composition requires time-consuming manual segmentation of CT images by an expert reader. In this work, we describe a two-step process to fully automate the analysis of CT body composition using a DenseNet to select the CT slice and U-Net to perform segmentation. We train and test our methods on independent cohorts. Our results show Dice scores (0.95-0.98) and correlation coefficients (R=0.99) that are favorable compared to human readers. These results suggest that fully automated body composition analysis is feasible, which could enable both clinical use and large-scale population studies

Identification of residues in ABCG2 affecting protein trafficking and drug transport, using co-evolutionary analysis of ABCG sequences

ABCG2 is an ABC (ATP-binding cassette) transporter with a physiological role in urate transport in the kidney and is also implicated in multi-drug efflux from a number of organs in the body. The trafficking of the protein and the mechanism by which it recognizes and transports diverse drugs are important areas of research. In the current study, we have made a series of single amino acid mutations in ABCG2 on the basis of sequence analysis. Mutant isoforms were characterized for cell surface expression and function. One mutant (I573A) showed disrupted glycosylation and reduced trafficking kinetics. In contrast with many ABC transporter folding mutations which appear to be ‘rescued’ by chemical chaperones or low temperature incubation, the I573A mutation was not enriched at the cell surface by either treatment, with the majority of the protein being retained in the endoplasmic reticulum (ER). Two other mutations (P485A and M549A) showed distinct effects on transport of ABCG2 substrates reinforcing the role of TM helix 3 in drug recognition and transport and indicating the presence of intracellular coupling regions in ABCG2