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The androgen receptor (AR) signaling pathway regulates the progression of prostate cancer (PC). Metastatic castration-resistant prostate cancer (mCRPC) patients generally receive AR-targeted therapies (ART) or androgen-deprivation therapies (ADT) with the initial response; however, resistance is inevitably observed. Prior studies have shown activity and upregulation of a family of androgen production, uptake, and conversion – APUC genes – based on genomic analyses of patient germlines. Genetic variants of some APUC genes, such as the conversion gene, HSD3B1, predict response to second-generation androgen-targeted therapies. Studies have begun to elucidate the overall role of APUC genes, each with unique actionable enzymatic activity, in mCRPC patient outcomes. The current role and knowledge of the genetic and genomic features of APUC genes in advanced prostate cancer and beyond are discussed in this review. These studies inform of how interpreting behavior of APUC genes through genomic tools will impact the treatment of advanced prostate cancer.

Programmable networking is evolving from programmable control plane solutions such as OpenFlow-based software-defined networking (SDN) to programmable data planes such as P4-based SDN. To support the functionality of the SDN, the correct view of the network topology is required. However, multiple attacks aimed at topology poisoning have been demonstrated in SDNs. While several controller-centralised security solutions have been proposed to defeat topology poisoning attacks, some attacks e.g., the Data Plane ARP Cache Poisoning Attack and the relay-type Link Fabrication Attack are difficult to detect using a fully centralised security solution. In this paper, we present the Security-Aware Programmable (SECAP) Switch—a lightweight, in-network, P4-based security solution that is designed to prevent attacks that might otherwise evade control plane solutions. The SECAP switch verifies source address details contained within the headers of protocols commonly used to perform topology poisoning attacks. This function is supported by a novel variance-based anomaly detection solution to provide a layered defence. We demonstrate the ability of the SECAP switch to defeat topology poisoning attacks with minimal memory and processing overhead.

Here we present two robotic sample changers integrated into the experimental stations for the macromolecular crystallography (MX) beamlines AMX and FMX, and the biological small-angle scattering (bioSAXS) beamline LiX. They enable fully automated unattended data collection and remote access to the beamlines. The system designs incorporate high-throughput, versatility, high-capacity, resource sharing and robustness. All systems are centered around a six-axis industrial robotic arm coupled with a force torque sensor and in-house end effectors (grippers). They have the same software architecture and the facility standard EPICS-based BEAST alarm system. The MX system is compatible with SPINE bases and Unipucks. It comprises a liquid nitrogen dewar holding 384 samples (24 Unipucks) and a stay-cold gripper, and utilizes machine vision software to track the sample during operations and to calculate the final mount position on the goniometer. The bioSAXS system has an in-house engineered sample storage unit that can hold up to 360 samples (20 sample holders) which keeps samples at a user-set temperature (277 K to 300 K). The MX systems were deployed in early 2017 and the bioSAXS system in early 2019.

KAMO and BLEND provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending BLEND to support additional clustering options such as the use of more accurate cell distance metrics and the use of reflection-intensity correlation coefficients to infer `distances' among sets of reflections. This increases the sensitivity to differences in unit-cell parameters and allows clustering to assemble nearly complete data sets on the basis of intensity or amplitude differences. If the data sets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. When starting from incomplete data sets, one applies KAMO twice, first using unit-cell parameters. In this step, either the simple cell vector distance of the original BLEND or the more sensitive NCDist is used. This step tends to find clusters of sufficient size such that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between reflections with a common hkl to merge clusters in a way that is sensitive to structural differences that may not have perturbed the unit-cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent of one another, it should be possible to greatly improve the efficacy of data-clustering software by using a multi-stage partitioning strategy. Here, one possible approach to multi-stage data clustering is demonstrated. The strategy is to use unit-cell clustering until the merged data are sufficiently complete and then to use intensity-based clustering. Using this strategy, it is demonstrated that it is possible to accurately cluster data sets from crystals that have subtle differences.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), threatens global public health. The world needs rapid development of new antivirals and vaccines to control the current pandemic and to control the spread of the variants. Among the proteins synthesized by the SARS-CoV-2 genome, main protease (Mpro also known as 3CLpro) is a primary drug target, due to its essential role in maturation of the viral polyproteins. In this study, we provide crystallographic evidence, along with some binding assay data, that three clinically approved anti hepatitis C virus drugs and two other drug-like compounds covalently bind to the Mpro Cys145 catalytic residue in the active site. Also, molecular docking studies can provide additional insight for the design of new antiviral inhibitors for SARS-CoV-2 using these drugs as lead compounds. One might consider derivatives of these lead compounds with higher affinity to the Mpro as potential COVID-19 therapeutics for further testing and possibly clinical trials.

Membrane proteins play essential roles in cellular function and metabolism. Nonetheless, biophysical and structural studies of membrane proteins are impeded by the difficulty of their expression in and purification from heterologous cell‐based systems. As an alternative to these cell‐based systems, cell‐free protein synthesis has proven to be an exquisite method for screening membrane protein targets in a variety of lipidic mimetics. Here we report a high‐throughput screening workflow and apply it to screen 61 eukaryotic membrane protein targets. For each target, we tested its expression in lipidic mimetics: two detergents, two liposomes, and two nanodiscs. We show that 35 membrane proteins (57%) can be expressed in a soluble fraction in at least one of the mimetics with the two detergents performing significantly better than nanodiscs and liposomes, in that order. Using the established cell‐free workflow, we studied the production and biophysical assays for mitochondrial pyruvate carrier (MPC) complexes. Our studies show that the complexes produced in cell‐free are functionally competent in complex formation and substrate binding. Our results highlight the utility of using cell‐free systems for screening and production of eukaryotic membrane proteins.

AAA+ proteases regulate numerous physiological and cellular processes through tightly regulated proteolytic cleavage of protein substrates driven by ATP hydrolysis. FtsH is the only known family of membrane-anchored AAA+ proteases essential for membrane protein quality control. Although a spiral staircase rotation mechanism for substrate translocation across the FtsH pore has been proposed, the detailed conformational changes among various states have not been clear due to absence of FtsH structures in these states. We report here the cryo-EM structure for Thermotoga maritima FtsH (TmFtsH) in a fully ADP-bound symmetric state. Comparisons of the ADP-state structure with its apo-state and a substrate-engaged yeast YME1 structure show conformational changes in the ATPase domains, rather than the protease domains. A reconstruction of the full-length TmFtsH provides structural insights for the dynamic transmembrane and the periplasmic domains. Our structural analyses expand the understanding of conformational switches between different nucleotide states in ATP hydrolysis by FtsH.

Zero Trust security model permits to secure cloud native applications while encrypting all network communication, authenticating, and authorizing every request. The service mesh can enable Zero Trust using a side-car proxy without changes to the application code. To the best of our knowledge, no previous work has provided a performance analysis of Zero Trust in a multi-cloud environment. This paper proposes a multi-cloud framework and a testing workflow to analyze performance of the data plane under load and the impact on the control plane, when Zero Trust is enabled. The results of preliminary tests show that Istio has reduced latency variability in responding to sequential HTTP requests. Results also reveal that the overall CPU and memory usage can increase based on service mesh configuration and the cloud environment., This paper was submitted for the IEEE CLOUD 2021 Conference and is currently being reviewed

A Python program to assemble radiation-damaged and partial diffraction data sets from microcrystals is described., The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation damaged with incomplete data per microcrystal and with unit-cell variations. A multi-stage data assembly method has previously been designed for microcrystal synchrotron crystallography. Here the strategy has been implemented as a Python program for microcrystal data assembly (PyMDA). PyMDA optimizes microcrystal data quality including weak anomalous signals through iterative crystal and frame rejections. Beyond microcrystals, PyMDA may be applicable for assembling data sets from larger crystals for improved data quality.

Both static and dynamic typing provide different benefits to the programmer. Statically typed languages support earlier type error detection and more opportunities for compiler optimizations. Dynamically typed languages facilitate the development of runtime adaptable applications and rapid prototyping. Since both approaches provide benefits, gradually typed languages support both typing approaches in the very same programming language. Gradual typing has been an active research field in the last years, turning out to be a strong influence on commercial languages. However, one important drawback of gradual typing is the runtime performance cost of the additional type checks performed at runtime. In this article, we propose a rule-based program specialization mechanism to provide significant performance optimizations of gradually typed code. Our system gathers dynamic type information of the application by simulating its execution. That type information is used to optimize the generated code, reducing the number of type checks performed at runtime. Moreover, program specialization allows the early detection of compile-time type errors, providing static type safety. To ensure the correctness of the proposed approach, we prove its soundness and efficiency properties. The specialization system has been implemented as part of a full-fledged programming language, measuring the runtime performance gain. The generated code performs significantly better than the state-of-the-art techniques to optimize dynamically typed code. Unlike the existing approaches, our system does not consume additional memory resources at runtime, because program specialization is performed statically. Program specialization involves an average compilation time increase from 2% to 11.75%.

Distributed Software-Defined Networks (SDNs) aim to maintain a consistent network state across members of the distributed control plane. This paper introduces a novel variation to the packet-in flood designed to target distributed SDNs that synchronise the network state in a strongly consistent manner. The Event Flooding Attack (EFA) takes advantage of the characteristics of a strong consistency model to enable an attacker to distribute the adverse effect of a DoS attack across a cluster, as well as engineer inconsistency between the true network state and the control plane’s view of this state. The impact of the attack is evaluated through experiments using an OpenDaylight cluster. It has been demonstrated on the testbed used in this work that an attacker can increase CPU consumption on all cluster nodes and cause inconsistency for a period of ≈ 55 s when 500 events are flooded at a frequency of 1/ms, while the same can be achieved for a period of ≈ 770 s when 2000 events are flooded at the same frequency. The impact of the attack is further demonstrated through it’s collaboration with, and simplification of, an existing host impersonation attack.

Low-energy native-SAD phasing from microcrystals of less than 10 micrometres in size is demonstrated at a synchrotron microdiffraction beamline., De novo structural evaluation of native biomolecules from single-wavelength anomalous diffraction (SAD) is a challenge because of the weakness of the anomalous scattering. The anomalous scattering from relevant native elements – primarily sulfur in proteins and phospho­rus in nucleic acids – increases as the X-ray energy decreases toward their K-edge transitions. Thus, measurements at a lowered X-ray energy are promising for making native SAD routine and robust. For microcrystals with sizes less than 10 µm, native-SAD phasing at synchrotron microdiffraction beamlines is even more challenging because of difficulties in sample manipulation, diffraction data collection and data analysis. Native-SAD analysis from microcrystals by using X-ray free-electron lasers has been demonstrated but has required use of thousands of thousands of microcrystals to achieve the necessary accuracy. Here it is shown that by exploitation of anomalous microdiffraction signals obtained at 5 keV, by the use of polyimide wellmounts, and by an iterative crystal and frame-rejection method, microcrystal native-SAD phasing is possible from as few as about 1 200 crystals. Our results show the utility of low-energy native-SAD phasing with microcrystals at synchrotron microdiffraction beamlines.

Metastatic castration-resistant prostate cancers (mCRPCs) are treated with therapies that antagonize the androgen receptor (AR). Nearly all patients develop resistance to AR-targeted therapies (ARTs). Our previous work identified CREB5 as an upregulated target gene in human mCRPC that promoted resistance to all clinically approved ART. The mechanisms by which CREB5 promotes progression of mCRPC or other cancers remains elusive. Integrating ChIP-seq and rapid immunoprecipitation and mass spectroscopy of endogenous proteins, we report that cells overexpressing CREB5 demonstrate extensive reprogramming of nuclear protein–protein interactions in response to the ART agent enzalutamide. Specifically, CREB5 physically interacts with AR, the pioneering actor FOXA1, and other known co-factors of AR and FOXA1 at transcription regulatory elements recently found to be active in mCRPC patients. We identified a subset of CREB5/FOXA1 co-interacting nuclear factors that have critical functions for AR transcription (GRHL2, HOXB13) while others (TBX3, NFIC) regulated cell viability and ART resistance and were amplified or overexpressed in mCRPC. Upon examining the nuclear protein interactions and the impact of CREB5 expression on the mCRPC patient transcriptome, we found that CREB5 was associated with Wnt signaling and epithelial to mesenchymal transitions, implicating these pathways in CREB5/FOXA1-mediated ART resistance. Overall, these observations define the molecular interactions among CREB5, FOXA1, and pathways that promote ART resistance.

Metastatic castration resistant prostate cancers (mCRPC) are treated with therapies that antagonize the androgen receptor (AR). Nearly all patients develop resistance to AR-targeted therapies (ART). Our previous work identified CREB5 as an upregulated target gene in human mCRPC that promoted resistance to all clinically-approved ART. The mechanisms by which CREB5 promotes progression of mCRPC or other cancers remains elusive. Integrating ChIP-seq and rapid immunoprecipitation and mass spectroscopy of endogenous proteins (RIME), we report that cells overexpressing CREB5 demonstrate extensive reprogramming of nuclear protein-protein interactions in response to the ART agent enzalutamide. Specifically, CREB5 physically interacts with AR, the pioneering actor FOXA1, and other known co-factors of AR and FOXA1 at transcription regulatory elements recently found to be active in mCRPC patients. We identified a subset of CREB5/FOXA1 co-interacting nuclear factors that have critical functions for AR transcription (GRHL2, HOXB13) while others (TBX3, NFIC) regulated cell viability and ART resistance and were amplified or overexpressed in mCRPC. Upon examining the nuclear protein interactions and the impact of CREB5 expression on the mCRPC patient transcriptome, we found CREB5 was associated with TGFβ and Wnt signaling and epithelial to mesenchymal transitions, implicating these pathways in ART resistance. Overall, these observations define the molecular interactions among CREB5, FOXA1, and pathways that promote ART resistance.

The trade in falsified medicine has increased significantly and it is estimated that global falsified sales have reached $100 billion in 2020. The EU Falsified Medicines Directive states that falsified medicines do not only reach patients through illegal routes but also via the legal supply chain. Falsified medicines can contain harmful ingredients. They can also contain too little or too much active ingredient or no active ingredient at all. BARDS (Broadband Acoustic Resonance Dissolution Spectroscopy) harnesses an acoustic phenomenon associated with the dissolution of a sample (tablet or powder). The resulting acoustic spectrum is unique and intrinsic to the sample and can be used as an identifier or signature profile. BARDS was evaluated in this study to determine whether a product is falsified or genuine in a rapid manner and at lower cost than many existing technologies. A range of genuine and falsified medicines, including falsified antimalarial tablets from south-east Asia, were tested, and compared to their counterpart genuine products. Significant differences between genuine and falsified doses were found in their acoustic signatures as they disintegrate and dissolve. Principal component analysis was employed to differentiate between the genuine and falsified medicines. This demonstrates that the tablets and capsules included here have intrinsic acoustic signatures which could be used to screen the quality of medicines.

Genetic variations in androgen metabolism may influence prostate cancer (PC) prognosis. Clinical studies consistently linked PC prognosis with four single nucleotide polymorphisms (SNPs) in the critical androgen-regulating genes: 3-beta-hydroxysteroid dehydrogenase (HSD3B1) rs1047303, 5-alpha-reductase 2 (SRD5A2) rs523349, and solute carrier organic ion (SLCO2B1) rs1789693 and rs12422149. We tested the association of four androgen-regulating SNPs, individually and combined, with PC-specific mortality in the ARIC population-based prospective cohort. Men diagnosed with PC (N = 622, 79% White, 21% Black) were followed for death (N = 350) including PC death (N = 74). Cox proportional hazards regression was used to estimate hazard ratios (HR) and 95%CI adjusting for center, age, stage, and grade at diagnosis using separate hazards for races. A priori genetic risk score (GRS) was created as the unweighted sum of risk alleles in the four pre-selected SNPs. The gain-of-function rs1047303C allele was associated PC-specific mortality among men with metastatic PC at diagnosis (HR = 4.89 per risk allele, p = 0.01). Higher GRS was associated with PC-specific mortality (per risk allele: HR = 1.26, p = 0.03). We confirmed that the gain-of-function allele in HSD3B1 rs1047303 is associated with greater PC mortality in men with metastatic disease. Additionally, our findings suggest a cumulative effect of androgen-regulating genes on PC-specific mortality, however, further validation is required.

The COVID-19 pandemic caused by the SARS-CoV-2 virus has created a global health and economic emergency. SARS-CoV-2 viruses hijack human proteins to promote their spread and virulence including the interactions involving the viral envelope (E) protein and human proteins. To understand the structural basis for SARS-CoV-2 viral-host recognition, we used cryo-electron microscopy to determine a structure for the human cell junction protein PALS1 and SARS-CoV-2 E protein complex. The structure shows that the E protein C-terminal DLLV motif recognizes a pocket formed exclusively by hydrophobic residues from the PDZ and SH3 domains in PALS1. Our structural analysis provides an explanation for the observation that the viral E protein recruits PALS1 from lung epithelial cell junctions resulting in vascular leakage, lung damage, viral spread, and virulence. In addition, our structure provides novel targets for peptide- and small-molecule inhibitors that could block the PALS1-E interactions to reduce the E-mediated damage to vascular structures.

Two new macromolecular crystallography (MX) beamlines at the National Synchrotron Light Source II, FMX and AMX, opened for general user operation in February 2017 [Schneider et al. (2013). J. Phys. Conf. Ser. 425, 012003; Fuchs et al. (2014). J. Phys. Conf. Ser. 493, 012021; Fuchs et al. (2016). AIP Conf. Proc. SRI2015, 1741, 030006]. FMX, the micro-focusing Frontier MX beamline in sector 17-ID-2 at NSLS-II, covers a 5–30 keV photon energy range and delivers a flux of 4.0 × 1012 photons s−1 at 1 Å into a 1 µm × 1.5 µm to 10 µm × 10 µm (V × H) variable focus, expected to reach 5 × 1012 photons s−1 at final storage-ring current. This flux density surpasses most MX beamlines by nearly two orders of magnitude. The high brightness and microbeam capability of FMX are focused on solving difficult crystallographic challenges. The beamline's flexible design supports a wide range of structure determination methods – serial crystallography on micrometre-sized crystals, raster optimization of diffraction from inhomogeneous crystals, high-resolution data collection from large-unit-cell crystals, room-temperature data collection for crystals that are difficult to freeze and for studying conformational dynamics, and fully automated data collection for sample-screening and ligand-binding studies. FMX's high dose rate reduces data collection times for applications like serial crystallography to minutes rather than hours. With associated sample lifetimes as short as a few milliseconds, new rapid sample-delivery methods have been implemented, such as an ultra-high-speed high-precision piezo scanner goniometer [Gao et al. (2018). J. Synchrotron Rad. 25, 1362–1370], new microcrystal-optimized micromesh well sample holders [Guo et al. (2018). IUCrJ, 5, 238–246] and highly viscous media injectors [Weierstall et al. (2014). Nat. Commun. 5, 3309]. The new beamline pushes the frontier of synchrotron crystallography and enables users to determine structures from difficult-to-crystallize targets like membrane proteins, using previously intractable crystals of a few micrometres in size, and to obtain quality structures from irregular larger crystals.

There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-quality 3D segmentations of these structures. Recently, methods based on deep learning have shown excellent results in automatic 3D segmentation, but they require large datasets for training, and there remains little consensus on which methods perform best. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem. A training set of 210 cross sectional CT images with kidney tumors was publicly released with corresponding semantic segmentation masks. 106 teams from five continents used this data to develop automated systems to predict the true segmentation masks on a test set of 90 CT images for which the corresponding ground truth segmentations were kept private. These predictions were scored and ranked according to their average So rensen-Dice coefficient between the kidney and tumor across all 90 cases. The winning team achieved a Dice of 0.974 for kidney and 0.851 for tumor, approaching the inter-annotator performance on kidney (0.983) but falling short on tumor (0.923). This challenge has now entered an "open leaderboard" phase where it serves as a challenging benchmark in 3D semantic segmentation., 24 pages, 11 figures

Objective: To understand better the public perception and comprehension of medical technology such as artificial intelligence (AI) and robotic surgery. In addition to this, to identify sensitivity to their use to ensure acceptability and quality of counseling. Subjects and Methods: A survey was conducted on a convenience sample of visitors to the MN Minnesota State Fair (n = 264). Participants were randomized to receive one of two similar surveys. In the first, a diagnosis was made by a physician and in the second by an AI application to compare confidence in human and computer-based diagnosis. Results: The median age of participants was 45 (interquartile range 28–59), 58% were female (n = 154) vs 42% male (n = 110), 69% had completed at least a bachelor's degree, 88% were Caucasian (n = 233) vs 12% ethnic minorities (n = 31) and were from 12 states, mostly from the Upper Midwest. Participants had nearly equal trust in AI vs physician diagnoses. However, they were significantly more likely to trust an AI diagnosis of cancer over a doctor's diagnosis when responding to the version of the survey that suggested that an AI could make medical diagnoses (p = 9.32e-06). Though 55% of respondents (n = 145) reported that they were uncomfortable with automated robotic surgery, the majority of the individuals surveyed (88%) mistakenly believed that partially autonomous surgery was already happening. Almost all (94%, n = 249) stated that they would be willing to pay for a review of medical imaging by an AI if available. Conclusion: Most participants express confidence in AI providing medical diagnoses, sometimes even over human physicians. Participants generally express concern with surgical AI, but they mistakenly believe that it is already being performed. As AI applications increase in medical practice, health care providers should be cognizant of the potential amount of misinformation and sensitivity that patients have to how such technology is represented.

Stability testing is essential in the pharmaceutical industry to determine product shelf- life and the conditions under which drug products should be stored. Stability testing involves a complex set of procedures, considerable cost, time, and scientific expertise to build quality, efficacy and safety in a drug formulation. This paper highlights a new complementary approach to stability testing called Broadband Acoustic Resonance Dissolution Spectroscopy (BARDS). BARDS measurements are based on reproducible changes in the compressibility of a solvent during dissolution. It is monitored acoustically via associated changes in the frequency of induced acoustic resonances. This study presents a novel approach to track the change of various drug formulations to determine the formulation's stability. Pellets, tablet and multiple-unit pellet system (MUPS) formulations were investigated to examine the effect of polymer coating and formulation core degradation over time. In combination with minimal usage of Ultra Violet - Visible Spectroscopy, BARDS can effectively track these changes. The technique offers a rapid approach to characterizing pharmaceutical formulations. BARDS can enable rapid development of solid drug formulation dissolution and disintegration testing as an In-Process Control test and drug stability analysis. The data show that a solid oral dose formulation has an intrinsic acoustic signature specific to the method of manufacture, excipient composition and elapsed time since the production of a product. BARDS data are also indicative of which aspect of a formulation may be unstable, whether a coating, sub-coating or core. It is potentially a time-efficient, cost-effective and greener approach to testing coating stability, disintegration and overall formulation stability.

INTRODUCTION AND OBJECTIVE:The radiographic characterization of renal masses is a promising direction toward improved risk stratification and treatment planning. A2016 exploratory study identified ...

INTRODUCTION AND OBJECTIVE:There are a growing number of uses of artificial intelligence (AI) in medicine. AI may have a significant impact on how patients understand and feel about their care. Pas...

INTRODUCTION AND OBJECTIVE:Renal tumor Contact Surface Area (CSA) is a sensible predictor for Partial Nephrectomy (PN) complexity. Three methods for estimating CSA have been proposed, but these hav...

A self-supervised workflow uses a 2D class average to progressively train a convolutional neural network for automated particle picking in cryo-EM., High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2.