Control of germ-band retraction in Drosophila by the zinc-finger protein HINDSIGHT

Drosophila embryos lacking hindsight gene function have a normal body plan and undergo normal germ-band extension. However, they fail to retract their germ bands. hindsight encodes a large nuclear protein of 1920 amino acids that contains fourteen C2H2-type zinc fingers, and glutamine-rich and proline-rich domains, suggesting that it functions as a transcription factor. Initial embryonic expression of hindsight RNA and protein occurs in the endoderm (midgut) and extraembryonic membrane (amnioserosa) prior to germ-band extension and continues in these tissues beyond the completion of germ-band retraction. Expression also occurs in the developing tracheal system, central and peripheral nervous systems, and the ureter of the Malpighian tubules. Strikingly, hindsight is not expressed in the epidermal ectoderm which is the tissue that undergoes the cell shape changes and movements during germ-band retraction. The embryonic midgut can be eliminated without affecting germ-band retraction. However, elimination of the amnioserosa results in the failure of germ-band retraction, implicating amnioserosal expression of hindsight as crucial for this process. Ubiquitous expression of hindsight in the early embryo rescues germ-band retraction without producing dominant gainof-function defects, suggesting that hindsight’s role in germ-band retraction is permissive rather than instructive. Previous analyses have shown that hindsight is required for maintenance of the differentiated amnioserosa (Frank, L. C. and Rushlow, C. (1996) Development 122, 1343-1352). Two classes of models are consistent with the present data. First, hindsight’s function in germ-band retraction may be limited to maintenance of the amnioserosa which then plays a physical role in the retraction process through contact with cells of the epidermal ectoderm. Second, hindsight might function both to maintain the amnioserosa and to regulate chemical signaling from the amnioserosa to the epidermal ectoderm, thus coordinating the cell shape changes and movements that drive germ-band retraction

Leveraging of single molecule sequencing methods for less invasive cancer detection

In the field of paediatric neuro-oncology, the positions of tumours within the central nervous system of the patients makes the acquisition of solid tumour biopsies risky. For many tumour types, monitoring of treatment response is restricted to Magnetic Resonance Imaging (MRI), or cerebrospinal fluid (CSF) cytology in the cases with leptomeningeal dissemination. Both of these lack sensitivity, leaving room for improvement. Recent advances in molecular barcoding sensitivity and error suppression have made the sequencing of DNA derived from liquid biopsies possible. Liquid biopsies offer an alternative to solid biopsies, since the collection of bodily fluids is much less invasive by comparison, and liquid biopsies contain cell-free DNA (cfDNA). In cancer patients, it has been shown that a fraction of the cfDNA in multiple liquid biopsies, such as plasma and CSF, harbour the genetic alterations present within the tumour. This circulating tumour DNA (ctDNA) can be used as a biomarker for diagnosis, stratification, and surveillance of the tumour. The monitoring of treatment response, and the detection of minimal residual disease, is of particular importance in paediatric brain tumours, given the low sensitivity of existing methods. This project created a versatile system, utilising molecular barcoding, which was able to detect Single Nucleotide Variants (SNVs), Insertions/Deletions and Copy-Number Variants in a single assay. A wet-lab workflow was created and iteratively improved, such that it could handle a diverse range of liquid biopsy types, including plasma, cystic fluid and CSF. This workflow was coupled with a bioinformatic pipeline, designed to process the data for all three variant calling processes simultaneously. For SNV calling, a custom variant caller was created to aid in the suppression of errors in barcoded sequencing, and the system was used in the first documented tracking of Adamantinomatous Craniopharyngioma treatment response using cystic fluid liquid biopsies

Small-Energy Rotational Transitions in Slow-Neutron Scattering by Water

A model which treats one rotational degree of freedom as hindered and the other as free and all translational degrees of freedom as hindered has been employed to calculate neutron differential scattering cross section of water in the region of small-energy transfers. The distribution is found to be sensitive to the presence of free-rotation transitions. It is suggested that such transitions present additional complexities in the study of molecular center-of-mass motions from high-resolution scattering data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86122/1/PhysRev.131.2547-RKO.pd

Parameter-Efficient Methods for Metastases Detection from Clinical Notes

Understanding the progression of cancer is crucial for defining treatments for patients. The objective of this study is to automate the detection of metastatic liver disease from free-style computed tomography (CT) radiology reports. Our research demonstrates that transferring knowledge using three approaches can improve model performance. First, we utilize generic language models (LMs), pretrained in a self-supervised manner. Second, we use a semi-supervised approach to train our model by automatically annotating a large unlabeled dataset; this approach substantially enhances the model's performance. Finally, we transfer knowledge from related tasks by designing a multi-task transfer learning methodology. We leverage the recent advancement of parameter-efficient LM adaptation strategies to improve performance and training efficiency. Our dataset consists of CT reports collected at Memorial Sloan Kettering Cancer Center (MSKCC) over the course of 12 years. 2,641 reports were manually annotated by domain experts; among them, 841 reports have been annotated for the presence of liver metastases. Our best model achieved an F1-score of 73.8%, a precision of 84%, and a recall of 65.8%.Comment: 6 pages, 1 figure, The 36th Canadian Conference on Artificial Intelligenc

Metallization for Yb14MnSb11-Based Thermoelectric Materials

Thermoelectric materials provide a means for converting heat into electrical power using a fully solid-state device. Power-generating devices (which include individual couples as well as multicouple modules) require the use of ntype and p-type thermoelectric materials, typically comprising highly doped narrow band-gap semiconductors which are connected to a heat collector and electrodes. To achieve greater device efficiency and greater specific power will require using new thermoelectric materials, in more complex combinations. One such material is the p-type compound semiconductor Yb14MnSb11 (YMS), which has been demonstrated to have one of the highest ZT values at 1,000 C, the desired operational temperature of many space-based radioisotope thermoelectric generators (RTGs). Despite the favorable attributes of the bulk YMS material, it must ultimately be incorporated into a power-generating device using a suitable joining technology. Typically, processes such as diffusion bonding and/or brazing are used to join thermoelectric materials to the heat collector and electrodes, with the goal of providing a stable, ohmic contact with high thermal conductivity at the required operating temperature. Since YMS is an inorganic compound featuring chemical bonds with a mixture of covalent and ionic character, simple metallurgical diffusion bonding is difficult to implement. Furthermore, the Sb within YMS readily reacts with most metals to form antimonide compounds with a wide range of stoichiometries. Although choosing metals that react to form high-melting-point antimonides could be employed to form a stable reaction bond, it is difficult to limit the reactivity of Sb in YMS such that the electrode is not completely consumed at an operating temperature of 1,000 C. Previous attempts to form suitable metallization layers resulted in poor bonding, complete consumption of the metallization layer or fracture within the YMS thermoelement (or leg)

Selective chemical probe inhibitor of Stat3, identified through structure-based virtual screening, induces antitumor activity

S31-201 (NSC 74859) is a chemical probe inhibitor of Stat3 activity, which was identified from the National Cancer Institute chemical libraries by using structure-based virtual screening with a computer model of the Stat3 SH2 domain bound to its Stat3 phosphotyrosine peptide derived from the x-ray crystal structure of the Stat3 beta homodimer. S31-201 inhibits Stat3-Stat3 complex formation and Stat3 DNA-binding and transcriptional activities. Furthermore, S31-201 inhibits growth and induces apoptosis preferentially in tumor cells that contain persistently activated Stat3. Constitutively climerized and active Stat3C and Stat3 SH2 domain rescue tumor cells from S31-201-induced apoptosis. Finally, S31-201 inhibits the expression of the Stat3-regulated genes encoding cyclin D1, BcI-xL, and survivin and inhibits the growth of human breast tumors in vivo. These findings strongly suggest that the antitumor activity of S31-201 is mediated in part through inhibition of aberrant Stat3 activation and provide the proof-of-concept for the potential clinical use of Stat3 inhibitors such as S31-201 in tumors harboring aberrant Stat3

Lipid Profiles from Dried Blood Spots Reveal Lipidomic Signatures of Newborns Undergoing Mild Therapeutic Hypothermia after Hypoxic-Ischemic Encephalopathy.

Hypoxic-ischemic encephalopathy (HIE) is associated with perinatal brain injury, which may lead to disability or death. As the brain is a lipid-rich organ, various lipid species can be significantly impacted by HIE and these correlate with specific changes to the lipidomic profile in the circulation. Objective: To investigate the peripheral blood lipidomic signature in dried blood spots (DBS) from newborns with HIE. Using univariate analysis, multivariate analysis and sPLS-DA modelling, we show that newborns with moderate-severe HIE (n = 46) who underwent therapeutic hypothermia (TH) displayed a robust peripheral blood lipidomic signature comprising 29 lipid species in four lipid classes; namely phosphatidylcholine (PC), lysophosphatidylcholine (LPC), triglyceride (TG) and sphingomyelin (SM) when compared with newborns with mild HIE (n = 18). In sPLS-DA modelling, the three most discriminant lipid species were TG 50:3, TG 54:5, and PC 36:5. We report a reduction in plasma TG and SM and an increase in plasma PC and LPC species during the course of TH in newborns with moderate-severe HIE, compared to a single specimen from newborns with mild HIE. These findings may guide the research in nutrition-based intervention strategies after HIE in synergy with TH to enhance neuroprotection.NIHR Cambridge Biomedical Research Centre (146281) & Biotechnology and Biological Sciences Research Council (BB/P028195/1

Subpopulation Treatment Effect Pattern Plot (STEPP) methods with R and Stata

We introduce the stepp packages for R and Stata that implement the subpopulation treatment effect pattern plot (STEPP) method. STEPP is a nonparametric graphical tool aimed at examin- ing possible heterogeneous treatment effects in subpopulations defined on a continuous covariate or composite score. More pecifically, STEPP considers overlapping subpopulations defined with respect to a continuous covariate (or risk index) and it estimates a treatment effect for each subpopulation. It also produces confidence regions and tests for treatment effect heterogeneity among the subpopulations. The original method has been extended in different directions such as different survival contexts, outcome types, or more efficient procedures for identifying the overlapping subpopulations. In this paper, we also introduce a novel method to determine the number of subjects within the subpopulations by minimizing the variability of the sizes of the subpopulations generated by a specific parameter combination. We illustrate the packages using both synthetic data and publicly available data sets. The most intensive computations in R are implemented in Fortran, while the Stata version exploits the powerful Mata language