Applying automation and machine learning to scanning transmission electron microscopy

This work studies how the benefits of automation and machine learning can be applied to the creation, imaging and image analysis of scanning transmission electron microscopy (STEM) samples. Recrystallised tungsten tips are produced using a semi-automated multi-stage process for use as sample platforms in atomic electron tomography (AET). Two coating techniques are tested to see whether they may be viable methods of reducing sample oxidation. An automated microscope control software framework is presented and demonstrated in three different scenarios: the high-throughput acquisition of CdSe/CdS core-shell nanoparticles, the acquisition of CBED patterns of chiral tellurium nanoparticles and the search for candidate particles for alpha tomography. Finally, machine learning is used to classify the handedness of simulated chiral particles using stereopairs of simulated STEM projections. A 'weak labelling' approach is also demonstrated that takes advantage of the intrinsic nature of chirality to remove the need for manually labelling training datasets

Statistical 3D morphology characterization of vaterite microspheres produced by engineered <i>Escherichia coli</i>

Hollow vaterite microspheres are important materials for biomedical applications such as drug delivery and regenerative medicine owing to their biocompatibility, high specific surface area, and ability to encapsulate a large number of bioactive molecules and compounds. We demonstrated that hollow vaterite microspheres are produced by an Escherichia coli strain engineered with a urease gene cluster from the ureolytic bacteria Sporosarcina pasteurii in the presence of bovine serum albumin. We characterized the 3D nanoscale morphology of five biogenic hollow vaterite microspheres using 3D high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) tomography. Using automated high-throughput HAADF-STEM imaging across several sample tilt orientations, we show that the microspheres evolved from a smaller more ellipsoidal shape to a larger more spherical shape while the internal hollow core increased in size and remained relatively spherical, indicating that the microspheres produced by this engineered strain likely do not contain the bacteria. The statistical 3D morphology information demonstrates the potential for using biogenic calcium carbonate mineralization to produce hollow vaterite microspheres with controlled morphologies. Statement of significance: The nanoscale 3D structures of biomaterials determine their physical, chemical, and biological properties, however significant efforts are required to obtain a statistical understanding of the internal 3D morphology of materials without damaging the structures. In this study, we developed a non-destructive, automated technique that allows us to understand the nanoscale 3D morphology of many unique hollow vaterite microspheres beyond the spectroscopy methods that lack local information and microscopy methods that cannot interrogate the full 3D structure. The method allowed us to quantitatively correlate the external diameters and aspect ratios of vaterite microspheres with their hollow internal structures at the nanoscale. This work demonstrates the opportunity to use automated transmission electron microscopy to characterize nanoscale 3D morphologies of many biomaterials and validate the chemical and biological functionality of these materials.</p

Advanced Techniques in Automated High Resolution Scanning Transmission Electron Microscopy

Scanning transmission electron microscopy is a common tool used to study the atomic structure of materials. It is an inherently multimodal tool allowing for the simultaneous acquisition of multiple information channels. Despite its versatility, however, experimental workflows currently rely heavily on experienced human operators and can only acquire data from small regions of a sample at a time. Here, we demonstrate a flexible pipeline-based system for high-throughput acquisition of atomic-resolution structural data using a custom built sample stage and automation program. The program is capable of operating over many hours without human intervention improving the statistics of high-resolution experiments

Risk of metastatic disease using [F-18]PSMA-1007 PET/CT for primary prostate cancer staging

Background Accurate prostate cancer imaging is critical for patient management. Multiple studies have demonstrated superior diagnostic accuracy of [Ga-68]-PSMA-11 PET/CT over conventional imaging for disease detection, with validated clinical and biochemical predictors of disease detection. More recently [F-18]PSMA-1007 offers theoretical imaging advantages, but there is limited evidence of clinical and biochemical predictors of scan findings in the staging population. This study investigates the association of clinical variables with imaging characteristics among patients who underwent [F-18]PSMA-1007 PET/CT for primary staging of men with histopathologically confirmed prostate carcinoma. A retrospective review of 194 consecutive patients imaged between May 2019 to May 2020 was performed. Association between imaging variables (presence and distribution of metastatic disease, primary tumour SUVmax) and clinical variables (EAU risk criteria) were assessed using descriptive statistics, logistic regression model and ROC analysis. Results The median age, PSA level and ISUP grade were 70 years, 10 ng/mL and ISUP grade 3, respectively. There were 36.6% of patients with intermediate-risk and 60.8% of patients with high-risk disease. ISUP grade was associated with the presence of metastasis overall (p = 0.008) as well as regional nodal (p = 0.003), non-regional nodal (p = 0.041) and bone (p = 0.006) metastases. PSA level was associated with metastatic disease overall (p = 0.001), regional (p = 0.001) and non-regional nodal metastases (p = 0.004), but not with bone metastases (p = 0.087). There were too few visceral metastases for meaningful analysis. SUVmax of the primary prostatic tumour was associated with ISUP grade (p = 0.004), PSA level (p 20 ng/mL and ISUP grade > 3 had a specificity of 85% (95% CI 78-91%) and 60% (95% CI 50-68%) and a sensitivity of 36% (95% CI 25-49%) and 62% (95% CI 49-74%), respectively, for detection of metastatic disease. Conclusion Metastatic disease according to [F-18]PSMA-1007 PET/CT was associated with ISUP grade and PSA level. This is the largest study using [F-18]PSMA-1007 PET/CT to confirm a positive correlation of PSA level, ISUP grade and stage with primary prostate tumour SUVmax

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Predictors of Poor Perinatal Outcome following Maternal Perception of Reduced Fetal Movements: A Prospective Cohort Study

Background Maternal perception of reduced fetal movement (RFM) is associated with increased risk of stillbirth and fetal growth restriction (FGR). RFM is thought to represent fetal compensation to conserve energy due to insufficient oxygen and nutrient transfer resulting from placental insufficiency. Objective To identify predictors of poor perinatal outcome after maternal perception of reduced fetal movements (RFM). Design Prospective cohort study. Methods 305 women presenting with RFM after 28 weeks of gestation were recruited. Demographic factors and clinical history were recorded and ultrasound performed to assess fetal biometry, liquor volume and umbilical artery Doppler. A maternal serum sample was obtained for measurement of placentally-derived or modified proteins including: alpha fetoprotein (AFP), human chorionic gonadotrophin (hCG), human placental lactogen (hPL), ischaemia-modified albumin (IMA), pregnancy associated plasma protein A (PAPP-A) and progesterone. Factors related to poor perinatal outcome were determined by logistic regression. Results 22.1% of pregnancies ended in a poor perinatal outcome after RFM. The most common complication was small-for-gestational age infants. Pregnancy outcome after maternal perception of RFM was related to amount of fetal activity while being monitored, abnormal fetal heart rate trace, diastolic blood pressure, estimated fetal weight, liquor volume, serum hCG and hPL. Following multiple logistic regression abnormal fetal heart rate trace (Odds ratio 7.08, 95% Confidence Interval 1.31–38.18), (OR) diastolic blood pressure (OR 1.04 (95% CI 1.01–1.09), estimated fetal weight centile (OR 0.95, 95% CI 0.94–0.97) and log maternal serum hPL (OR 0.13, 95% CI 0.02–0.99) were independently related to pregnancy outcome. hPL was related to placental mass. Conclusion Poor perinatal outcome after maternal perception of RFM is closely related to factors which are connected to placental dysfunction. Novel tests of placental function and associated fetal response may provide improved means to detect fetuses at greatest risk of poor perinatal outcome after RFM

MAKING ANIMALS ALCOHOLIC: SHIFTING LABORATORY MODELS OF ADDICTION

The use of animals as experimental organisms has been critical to the development of addiction research from the nineteenth century. They have been used as a means of generating reliable data regarding the processes of addiction that was not available from the study of human subjects. Their use, however, has been far from straightforward. Through focusing on the study of alcoholism, where the nonhuman animal proved a most reluctant collaborator, this paper will analyze the ways in which scientists attempted to deal with its determined sobriety and account for their consistent failure to replicate the volitional consumption of ethanol to the point of physical dependency. In doing so, we will see how the animal model not only served as a means of interrogating a complex pathology, but also came to embody competing definitions of alcoholism as a disease process, and alternative visions for the very structure and purpose of a research field

Oxidative protein labeling in mass-spectrometry-based proteomics

Oxidation of proteins and peptides is a common phenomenon, and can be employed as a labeling technique for mass-spectrometry-based proteomics. Nonspecific oxidative labeling methods can modify almost any amino acid residue in a protein or only surface-exposed regions. Specific agents may label reactive functional groups in amino acids, primarily cysteine, methionine, tyrosine, and tryptophan. Nonspecific radical intermediates (reactive oxygen, nitrogen, or halogen species) can be produced by chemical, photochemical, electrochemical, or enzymatic methods. More targeted oxidation can be achieved by chemical reagents but also by direct electrochemical oxidation, which opens the way to instrumental labeling methods. Oxidative labeling of amino acids in the context of liquid chromatography(LC)–mass spectrometry (MS) based proteomics allows for differential LC separation, improved MS ionization, and label-specific fragmentation and detection. Oxidation of proteins can create new reactive groups which are useful for secondary, more conventional derivatization reactions with, e.g., fluorescent labels. This review summarizes reactions of oxidizing agents with peptides and proteins, the corresponding methodologies and instrumentation, and the major, innovative applications of oxidative protein labeling described in selected literature from the last decade

CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe