Patterned probes for high precision 4D-STEM bragg measurements.

Nanoscale strain mapping by four-dimensional scanning transmission electron microscopy (4D-STEM) relies on determining the precise locations of Bragg-scattered electrons in a sequence of diffraction patterns, a task which is complicated by dynamical scattering, inelastic scattering, and shot noise. These features hinder accurate automated computational detection and position measurement of the diffracted disks, limiting the precision of measurements of local deformation. Here, we investigate the use of patterned probes to improve the precision of strain mapping. We imprint a "bullseye" pattern onto the probe, by using a binary mask in the probe-forming aperture, to improve the robustness of the peak finding algorithm to intensity modulations inside the diffracted disks. We show that this imprinting leads to substantially improved strain-mapping precision at the expense of a slight decrease in spatial resolution. In experiments on an unstrained silicon reference sample, we observe an improvement in strain measurement precision from 2.7% of the reciprocal lattice vectors with standard probes to 0.3% using bullseye probes for a thin sample, and an improvement from 4.7% to 0.8% for a thick sample. We also use multislice simulations to explore how sample thickness and electron dose limit the attainable accuracy and precision for 4D-STEM strain measurements

Promoting Teamwork in Translational Medical Teams: Insights and Recommendations from Science and Practice

Translational medical teams are transdisciplinary, highly collaborative, and operate within dynamic environments to solve time-sensitive and complex problems. These teams are tasked with turning observations in the laboratory and clinic into effective interventions that improve the health of individuals and the public. The nature of the problems they seek to solve requires coordination among clinicians, scientists, and experts from various scientific disciplines. Characteristically, translational medical teams have complex compositions, structure, and pluralistic goals, which pose significant challenges and barriers to enacting effective teamwork, compromising team performance. Given these challenges, it is imperative to glean insights from teams research and the science of team science on how to execute efficacious teamwork. Consequently, the purpose of this paper is to discuss specific teamwork processes (i.e., trust, communication, self-correction, backup behavior, shared mental models, and conflict management) that are critical to translational medical team performance and offer mechanisms to better equip such teams. Utilizing a theoretical framework of transdisciplinary teamwork adapted from the science of team science and tailored to translational medical teams, we describe each of these processes, their relation to translational medical team outcomes, and how they can be leveraged to improve teamwork. Such a discussion aims to provide practical guidance for conceptualizing and enhancing teamwork in translational medical teams

A Comparison of Homemade Phantoms for Ultrasound Guided Peripheral Intravenous Catheter Insertion

Purpose: Ultrasound (U/S) guided peripheral intravenous catheter (PIV) placement is implemented in clinical settings across the medical field, with evidence supporting the use of point-of-care U/S as a procedural tool to improve patient outcomes. Non-commercial vascular access phantoms made of various materials have been described in published literature and online tutorials; however, there has been no comparison of the models. The primary objective of this study is to determine if non-commercial phantoms are useful for the education of U/S guided PIV placement. Methods: This prospective observational study trialed six unique phantom models: 1) the Amini ballistics gel model, 2) the Morrow ballistics gel model, 3) the University of California San Diego (UCSD) gelatin model, 4) the Rippey chicken model, 5) the Nolting spam model, 6) and the Johnson tofu model. Selected phantoms were assembled through instructions from the source reference. Six U/S fellowship trained Emergency Medicine physicians performed U/S guided PIV placement on each model to evaluate their effectiveness pertaining to phantom haptics, echogenicity properties, and utility for PIV practice. Results/ Conclusion: The Rippey model outperformed other models in this study, doing so with a mid-level cost and minimal preparation time. The Rippey model scored highest on aggregate scores pertaining to haptics, echogenicity, and utility for U/S guided PIV placement and comparability to commercial products. Non-commercial U/S phantoms may represent cost-effective and useful PIV insertion educational tools. Future studies should investigate the utility of these phantoms in teaching USIV to novice learners and direct comparison of non-commercial to commercial phantoms

py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full 2D image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields and other sample-dependent properties. However, extracting this information requires complex analysis pipelines, from data wrangling to calibration to analysis to visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail, and present results from several experimental datasets. We have also implemented a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open source HDF5 standard. We hope this tool will benefit the research community, helps to move the developing standards for data and computational methods in electron microscopy, and invite the community to contribute to this ongoing, fully open-source project

A genetic fingerprint of Amphipoda from Icelandic waters – the baseline for further biodiversity and biogeography studies

Source at https://doi.org/10.3897/zookeys.731.19931.Amphipods constitute an abundant part of Icelandic deep-sea zoobenthos yet knowledge of the diversity of this fauna, particularly at the molecular level, is scarce. The present work aims to use molecular methods to investigate genetic variation of the Amphipoda sampled during two IceAGE collecting expeditions. The mitochondrial cytochrome oxidase subunit 1 (COI) of 167 individuals originally assigned to 75 morphospecies was analysed. These targeted morhospecies were readily identifiable by experts using light microscopy and representative of families where there is current ongoing taxonomic research. The study resulted in 81 Barcode Identity Numbers (BINs) (of which >90% were published for the first time), while Automatic Barcode Gap Discovery revealed the existence of 78 to 83 Molecular Operational Taxonomic Units (MOTUs). Six nominal species (Rhachotropis helleri, Arrhis phyllonyx, Deflexilodes tenuirostratus, Paroediceros propinquus, Metopa boeckii, Astyra abyssi) appeared to have a molecular variation higher than the 0.03 threshold of both p-distance and K2P usually used for amphipod species delineation. Conversely, two Oedicerotidae regarded as separate morphospecies clustered together with divergences in the order of intraspecific variation. The incongruence between the BINs associated with presently identified species and the publicly available data of the same taxa was observed in case of Paramphithoe hystrix and Amphilochus manudens. The findings from this research project highlight the necessity of supporting molecular studies with thorough morphology species analyses

Membrane transporter dimerization driven by differential lipid solvation energetics of dissociated and associated states

Over two-thirds of integral membrane proteins of known structure assemble into oligomers. Yet, the forces that drive the association of these proteins remain to be delineated, as the lipid bilayer is a solvent environment that is both structurally and chemically complex. In this study, we reveal how the lipid solvent defines the dimerization equilibrium of the CLC-ec1 C

Combined inhibition of BCL-2 and MCL-1 overcomes BAX deficiency-mediated resistance of TP53-mutant acute myeloid leukemia to individual BH3 mimetics

TP53-mutant acute myeloid leukemia (AML) respond poorly to currently available treatments, including venetoclax-based drug combinations and pose a major therapeutic challenge. Analyses of RNA sequencing and reverse phase protein array datasets revealed significantly lower BAX RNA and protein levels in TP53-mutant compared to TP53-wild-type (WT) AML, a finding confirmed in isogenic CRISPR-generated TP53-knockout and -mutant AML. The response to either BCL-2 (venetoclax) or MCL-1 (AMG176) inhibition was BAX-dependent and much reduced in TP53-mutant compared to TP53-WT cells, while the combination of two BH3 mimetics effectively activated BAX, circumventing survival mechanisms in cells treated with either BH3 mimetic, and synergistically induced cell death in TP53-mutant AML and stem/progenitor cells. The BH3 mimetic-driven stress response and cell death patterns after dual inhibition were largely independent of TP53 status and affected by apoptosis induction. Co-targeting, but not individual targeting of BCL-2 and MCL-1 in mice xenografted with TP53-WT and TP53-R248W Molm13 cells suppressed both TP53-WT and TP53-mutant cell growth and significantly prolonged survival. Our results demonstrate that co-targeting BCL-2 and MCL-1 overcomes BAX deficiency-mediated resistance to individual BH3 mimetics in TP53-mutant cells, thus shifting cell fate from survival to death in TP53-deficient and -mutant AML. This concept warrants clinical evaluation

The cellular and synaptic architecture of the mechanosensory dorsal horn

The deep dorsal horn is a poorly characterized spinal cord region implicated in processing low-threshold mechanoreceptor (LTMR) information. We report an array of mouse genetic tools for defining neuronal components and functions of the dorsal horn LTMR-recipient zone (LTMR-RZ), a role for LTMR-RZ processing in tactile perception, and the basic logic of LTMR-RZ organization. We found an unexpectedly high degree of neuronal diversity in the LTMR-RZ: seven excitatory and four inhibitory subtypes of interneurons exhibiting unique morphological, physiological, and synaptic properties. Remarkably, LTMRs form synapses on between four and 11 LTMR-RZ interneuron subtypes, while each LTMR-RZ interneuron subtype samples inputs from at least one to three LTMR classes, as well as spinal cord interneurons and corticospinal neurons. Thus, the LTMR-RZ is a somatosensory processing region endowed with a neuronal complexity that rivals the retina and functions to pattern the activity of ascending touch pathways that underlie tactile perception

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio