Antibody discovery identifies regulatory mechanisms of protein arginine deiminase 4.

Unlocking the potential of protein arginine deiminase 4 (PAD4) as a drug target for rheumatoid arthritis requires a deeper understanding of its regulation. In this study, we use unbiased antibody selections to identify functional antibodies capable of either activating or inhibiting PAD4 activity. Through cryogenic-electron microscopy, we characterized the structures of these antibodies in complex with PAD4 and revealed insights into their mechanisms of action. Rather than steric occlusion of the substrate-binding catalytic pocket, the antibodies modulate PAD4 activity through interactions with allosteric binding sites adjacent to the catalytic pocket. These binding events lead to either alteration of the active site conformation or the enzyme oligomeric state, resulting in modulation of PAD4 activity. Our study uses antibody engineering to reveal new mechanisms for enzyme regulation and highlights the potential of using PAD4 agonist and antagonist antibodies for studying PAD4-dependency in disease models and future therapeutic development.

Adsorption at Nanoconfined Solid-Water Interfaces.

Reactions at solid-water interfaces play a foundational role in water treatment systems, catalysis, and chemical separations, and in predicting chemical fate and transport in the environment. Over the last century, experimental measurements and computational models have made tremendous progress in capturing reactions at solid surfaces. The interfacial reactivity of a solid surface, however, can change dramatically and unexpectedly when it is confined to the nanoscale. Nanoconfinement can arise in different geometries such as pores/cages (3D confinement), channels (2D confinement), and slits (1D confinement). Therefore, measurements on unconfined surfaces, and molecular models parameterized based on these measurements, fail to capture chemical behaviors under nanoconfinement. This review evaluates recent experimental and theoretical advances, with a focus on adsorption at solid-water interfaces. We review how nanoconfinement alters the physico-chemical properties of water, and how the structure and dynamics of nanoconfined water dictate energetics, pathways, and products of adsorption in nanopores. Finally, the implications of these findings and future research directions are discussed.

MFGE8 in exosomes derived from mesenchymal stem cells prevents esophageal stricture after endoscopic submucosal dissection in pigs.

BACKGROUND: Endoscopic submucosal dissection (ESD) is the current standard treatment for early-stage esophageal neoplasms. However, the postoperative esophageal stricture after extensive mucosal dissection remains a severe challenge with limited effective treatments available. In this study, we introduced a chitosan/gelatin (ChGel) sponge encapsulating the adipose mesenchymal stem cells (ADMSCs)-derived exosomes (ChGelMSC-Exo) for the prevention of esophageal stenosis after ESD in a porcine model. RESULTS: Pigs were randomly assigned into (1) ChGelMSC-Exo treatment group, (2) ChGelPBS group, and (3) the controls. Exosome treatments were applied immediately on the day after ESD as well as on day 7. Exosome components crucial for wound healing were investigated by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and small RNA sequencing. ChGelMSC-Exo treatment significantly reduced mucosal contraction on day 21, with less fiber accumulation and inflammatory infiltration, and enhanced angiogenesis when compared with the control and ChGelPBS groups. The anti-fibrotic effects following MSC-Exo treatment were further found to be associated with the anti-inflammatory M2 polarization of the resident macrophages, especially within the M2b subset characterized by the reduced TGFß1 secretion, which sufficiently inhibited inflammation and prevented the activation of myofibroblast with less collagen production at the early stage after ESD. Moreover, the abundant expression of exosomal MFGE8 was identified to be involved in the transition of the M2b-macrophage subset through the activation of MFGE8/STAT3/Arg1 axis. CONCLUSIONS: Our study demonstrates that exosomal MFGE8 significantly promotes the polarization of the M2b-macrophage subset, consequently reducing collagen deposition. These findings suggest a promising potential for MSC-Exo therapy in preventing the development of esophageal stricture after near-circumferential ESD.

Discovery Proteomics Analysis Determines That Driver Oncogenes Suppress Antiviral Defense Pathways Through Reduction in Interferon-ß Autocrine Stimulation.

Since the discovery of oncogenes, there has been tremendous interest to understand their mechanistic basis and to develop broadly actionable therapeutics. Some of the most frequently activated oncogenes driving diverse cancers are c-MYC, EGFR, HER2, AKT, KRAS, BRAF, and MEK. Using a reductionist approach, we explored how cellular proteomes are remodeled in isogenic cell lines engineered with or without these driver oncogenes. The most striking discovery for all oncogenic models was the systematic downregulation of scores of antiviral proteins regulated by type 1 interferon. These findings extended to cancer cell lines and patient-derived xenograft models of highly refractory pancreatic cancer and osteosarcoma driven by KRAS and MYC oncogenes. The oncogenes reduced basal expression of and autocrine stimulation by type 1 interferon causing remarkable convergence on common phenotypic and functional profiles. In particular, there was dramatically lower expression of dsRNA sensors including DDX58 (RIG-I) and OAS proteins, which resulted in attenuated functional responses when the oncogenic cells were treated with the dsRNA mimetic, polyI:C, and increased susceptibility to infection with an RNA virus shown using SARS-CoV-2. Our reductionist approach provides molecular and functional insights connected to immune evasion hallmarks in cancers and suggests therapeutic opportunities.

Large remodeling of the Myc-induced cell surface proteome in B cells and prostate cells creates new opportunities for immunotherapy.

MYC is a powerful transcription factor overexpressed in many human cancers including B cell and prostate cancers. Antibody therapeutics are exciting opportunities to attack cancers but require knowledge of surface proteins that change due to oncogene expression. To identify how MYC overexpression remodels the cell surface proteome in a cell autologous fashion and in different cell types, we investigated the impact of MYC overexpression on 800 surface proteins in three isogenic model cell lines either of B cell or prostate cell origin engineered to have high or low MYC levels. We found that MYC overexpression resulted in dramatic remodeling (both up- and down-regulation) of the cell surfaceome in a cell type-dependent fashion. We found systematic and large increases in distinct sets of >80 transporters including nucleoside transporters and nutrient transporters making cells more sensitive to toxic nucleoside analogs like cytarabine, commonly used for treating hematological cancers. Paradoxically, MYC overexpression also increased expression of surface proteins driving cell turnover such as TNFRSF10B, also known as death receptor 5, and immune cell attacking signals such as the natural killer cell activating ligand NCR3LG1, also known as B7-H6. We generated recombinant antibodies to these two targets and verified their up-regulation in MYC overexpression cell lines and showed they were sensitive to bispecific T cell engagers (BiTEs). Our studies demonstrate how MYC overexpression leads to dramatic bidirectional remodeling of the surfaceome in a cell type-dependent but functionally convergent fashion and identify surface targets or combinations thereof as possible candidates for cytotoxic metabolite or immunotherapy.

Finding Infinities in Nanoconfined Geothermal Electrolyte Static Dielectric Properties and Implications on Ion Adsorption/Pairing.

Infinities should naturally occur in the dielectric responses of ionic solutions relevant to many geochemical, energy storage, and electrochemical applications at a strictly zero frequency. Using molecular dynamics simulations cross-referenced with coarse-grained Monte Carlo models, using nanoslit pore models at hydrothermal conditions, and treating confined mobile charges as polarization, we demonstrate the far reaching consequences. The dielectric permittivity profile perpendicular to the slit (ϵ⊥(z)) increases, not decreases, with ionic concentration, unlike in the more widely studied megahertz-to-gigahertz frequency range. In confined electrolytes, the divergences in ϵ⊥(z) correctly describe crossovers between bulk- and surface-dominated dielectric behavior. Nanoconfinement at low ionic concentrations changes monovalent ion energetics by 1-2 kJ/mol, but no dielectric property studied so far is universally correlated to ion adsorption or ion-ion interactions. We caution that infinities signal violation of the "electrical insulator" dielectric assumption.

Engaging a Non-catalytic Cysteine Residue Drives Potent and Selective Inhibition of Caspase-6.

Caspases are a family of cysteine-dependent proteases with important cellular functions in inflammation and apoptosis, while also implicated in human diseases. Classical chemical tools to study caspase functions lack selectivity for specific caspase family members due to highly conserved active sites and catalytic machinery. To overcome this limitation, we targeted a non-catalytic cysteine residue (C264) unique to caspase-6 (C6), an enigmatic and understudied caspase isoform. Starting from disulfide ligands identified in a cysteine trapping screen, we used a structure-informed covalent ligand design to produce potent, irreversible inhibitors (3a) and chemoproteomic probes (13-t) of C6 that exhibit unprecedented selectivity over other caspase family members and high proteome selectivity. This approach and the new tools described will enable rigorous interrogation of the role of caspase-6 in developmental biology and in inflammatory and neurodegenerative diseases.

Bi-paratopic and multivalent VH domains block ACE2 binding and neutralize SARS-CoV-2.

Neutralizing agents against SARS-CoV-2 are urgently needed for the treatment and prophylaxis of COVID-19. Here, we present a strategy to rapidly identify and assemble synthetic human variable heavy (VH) domains toward neutralizing epitopes. We constructed a VH-phage library and targeted the angiotensin-converting enzyme 2 (ACE2) binding interface of the SARS-CoV-2 Spike receptor-binding domain (Spike-RBD). Using a masked selection approach, we identified VH binders to two non-overlapping epitopes and further assembled these into multivalent and bi-paratopic formats. These VH constructs showed increased affinity to Spike (up to 600-fold) and neutralization potency (up to 1,400-fold) on pseudotyped SARS-CoV-2 virus when compared to standalone VH domains. The most potent binder, a trivalent VH, neutralized authentic SARS-CoV-2 with a half-maximal inhibitory concentration (IC50) of 4.0 nM (180 ng ml-1). A cryo-EM structure of the trivalent VH bound to Spike shows each VH domain engaging an RBD at the ACE2 binding site, confirming our original design strategy.

Deep profiling of protease substrate specificity enabled by dual random and scanned human proteome substrate phage libraries.

Proteolysis is a major posttranslational regulator of biology inside and outside of cells. Broad identification of optimal cleavage sites and natural substrates of proteases is critical for drug discovery and to understand protease biology. Here, we present a method that employs two genetically encoded substrate phage display libraries coupled with next generation sequencing (SPD-NGS) that allows up to 10,000-fold deeper sequence coverage of the typical six- to eight-residue protease cleavage sites compared to state-of-the-art synthetic peptide libraries or proteomics. We applied SPD-NGS to two classes of proteases, the intracellular caspases, and the ectodomains of the sheddases, ADAMs 10 and 17. The first library (Lib 10AA) allowed us to identify 104 to 105 unique cleavage sites over a 1,000-fold dynamic range of NGS counts and produced consensus and optimal cleavage motifs based position-specific scoring matrices. A second SPD-NGS library (Lib hP), which displayed virtually the entire human proteome tiled in contiguous 49 amino acid sequences with 25 amino acid overlaps, enabled us to identify candidate human proteome sequences. We identified up to 104 natural linear cut sites, depending on the protease, and captured most of the examples previously identified by proteomics and predicted 10- to 100-fold more. Structural bioinformatics was used to facilitate the identification of candidate natural protein substrates. SPD-NGS is rapid, reproducible, simple to perform and analyze, inexpensive, and renewable, with unprecedented depth of coverage for substrate sequences, and is an important tool for protease biologists interested in protease specificity for specific assays and inhibitors and to facilitate identification of natural protein substrates.

Broad and thematic remodeling of the surfaceome and glycoproteome on isogenic cells transformed with driving proliferative oncogenes.

The cell surface proteome, the surfaceome, is the interface for engaging the extracellular space in normal and cancer cells. Here we apply quantitative proteomics of N-linked glycoproteins to reveal how a collection of some 700 surface proteins is dramatically remodeled in an isogenic breast epithelial cell line stably expressing any of six of the most prominent proliferative oncogenes, including the receptor tyrosine kinases, EGFR and HER2, and downstream signaling partners such as KRAS, BRAF, MEK, and AKT. We find that each oncogene has somewhat different surfaceomes, but the functions of these proteins are harmonized by common biological themes including up-regulation of nutrient transporters, down-regulation of adhesion molecules and tumor suppressing phosphatases, and alteration in immune modulators. Addition of a potent MEK inhibitor that blocks MAPK signaling brings each oncogene-induced surfaceome back to a common state reflecting the strong dependence of the oncogene on the MAPK pathway to propagate signaling. Cell surface protein capture is mediated by covalent tagging of surface glycans, yet current methods do not afford sequencing of intact glycopeptides. Thus, we complement the surfaceome data with whole cell glycoproteomics enabled by a recently developed technique called activated ion electron transfer dissociation (AI-ETD). We found massive oncogene-induced changes to the glycoproteome and differential increases in complex hybrid glycans, especially for KRAS and HER2 oncogenes. Overall, these studies provide a broad systems-level view of how specific driver oncogenes remodel the surfaceome and the glycoproteome in a cell autologous fashion, and suggest possible surface targets, and combinations thereof, for drug and biomarker discovery.

Engineered ACE2 receptor traps potently neutralize SARS-CoV-2.

An essential mechanism for severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection begins with the viral spike protein binding to the human receptor protein angiotensin-converting enzyme II (ACE2). Here, we describe a stepwise engineering approach to generate a set of affinity optimized, enzymatically inactivated ACE2 variants that potently block SARS-CoV-2 infection of cells. These optimized receptor traps tightly bind the receptor binding domain (RBD) of the viral spike protein and prevent entry into host cells. We first computationally designed the ACE2-RBD interface using a two-stage flexible protein backbone design process that improved affinity for the RBD by up to 12-fold. These designed receptor variants were affinity matured an additional 14-fold by random mutagenesis and selection using yeast surface display. The highest-affinity variant contained seven amino acid changes and bound to the RBD 170-fold more tightly than wild-type ACE2. With the addition of the natural ACE2 collectrin domain and fusion to a human immunoglobulin crystallizable fragment (Fc) domain for increased stabilization and avidity, the most optimal ACE2 receptor traps neutralized SARS-CoV-2-pseudotyped lentivirus and authentic SARS-CoV-2 virus with half-maximal inhibitory concentrations (IC50s) in the 10- to 100-ng/mL range. Engineered ACE2 receptor traps offer a promising route to fighting infections by SARS-CoV-2 and other ACE2-using coronaviruses, with the key advantage that viral resistance would also likely impair viral entry. Moreover, such traps can be predesigned for viruses with known entry receptors for faster therapeutic response without the need for neutralizing antibodies isolated from convalescent patients.

Profiling the Surfaceome Identifies Therapeutic Targets for Cells with Hyperactive mTORC1 Signaling.

Aberrantly high mTORC1 signaling is a known driver of many cancers and human disorders, yet pharmacological inhibition of mTORC1 rarely confers durable clinical responses. To explore alternative therapeutic strategies, herein we conducted a proteomics survey to identify cell surface proteins upregulated by mTORC1. A comparison of the surfaceome from Tsc1-/-versus Tsc1+/+ mouse embryonic fibroblasts revealed 59 proteins predicted to be significantly overexpressed in Tsc1-/- cells. Further validation of the data in multiple mouse and human cell lines showed that mTORC1 signaling most dramatically induced the expression of the proteases neprilysin (NEP/CD10) and aminopeptidase N (APN/CD13). Functional studies showed that constitutive mTORC1 signaling sensitized cells to genetic ablation of NEP and APN, as well as the biochemical inhibition of APN. In summary, these data show that mTORC1 signaling plays a significant role in the constitution of the surfaceome, which in turn may present novel therapeutic strategies.

Multiomics of azacitidine-treated AML cells reveals variable and convergent targets that remodel the cell-surface proteome.

Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are diseases of abnormal hematopoietic differentiation with aberrant epigenetic alterations. Azacitidine (AZA) is a DNA methyltransferase inhibitor widely used to treat MDS and AML, yet the impact of AZA on the cell-surface proteome has not been defined. To identify potential therapeutic targets for use in combination with AZA in AML patients, we investigated the effects of AZA treatment on four AML cell lines representing different stages of differentiation. The effect of AZA treatment on these cell lines was characterized at three levels: the DNA methylome, the transcriptome, and the cell-surface proteome. Untreated AML cell lines showed substantial overlap at all three omics levels; however, while AZA treatment globally reduced DNA methylation in all cell lines, changes in the transcriptome and surface proteome were subtle and differed among the cell lines. Transcriptome analysis identified five commonly up-regulated coding genes upon AZA treatment in all four cell lines, TRPM4 being the only gene encoding a surface protein, and surface proteome analysis found no commonly regulated proteins. Gene set enrichment analysis of differentially regulated RNA and surface proteins showed a decrease in metabolic pathways and an increase in immune defense response pathways. As such, AZA treatment led to diverse effects at the individual gene and protein levels but converged to common responses at the pathway level. Given the heterogeneous responses in the four cell lines, we discuss potential therapeutic strategies for AML in combination with AZA.

Electron leakage through heterogeneous LiF on lithium-metal battery anodes.

The solid-electrolyte interphase (SEI) that forms on lithium ion battery (LIB) anodes prevents degradation-causing transfer of electrons to the electrolyte. Grain boundaries (GBs) between different SEI components, like LiF, have been suggested to accelerate Li+ transport. However, using the non-equilibrium Green's function technique with density functional theory (NEGF-DFT), we find that GBs enhance electron tunneling in thin LiF films by 1-2 orders of magnitude, depending on the bias. Extrapolating to thicker films using the Wentzel-Kramers-Brillouin (WKB) method emphasizes that safer batteries require passivation of GBs in the SEI.

Interplay of physically different properties leading to challenges in separating lanthanide cations - an ab initio molecular dynamics and experimental study.

Lanthanide elements have well-documented similarities in their chemical behavior, which make the valuable trivalent lanthanide cations (Ln3+) particularly difficult to separate from each other in water. In this work, we apply ab initio molecular dynamics simulations to compare the free energies (ΔGads) associated with the adsorption of lanthanide cations to silica surfaces at a pH condition where SiO- groups are present. The predicted ΔGads for lutetium (Lu3+) and europium (Eu3+) are similar within statistical uncertainties; this is in qualitative agreement with our batch adsorption measurements on silica. This finding is remarkable because the two cations exhibit hydration free energies (ΔGhyd) that differ by >2 eV, different hydration numbers, and different hydrolysis behavior far from silica surfaces. We observe that the similarity in Lu3+ and Eu3+ ΔGads is the result of a delicate cancellation between the difference in Eu3+ and Lu3+ hydration (ΔGhyd), and their difference in binding energies to silica. We propose that disrupting this cancellation at the two end points, either for adsorbed or completely desorbed lanthanides (e.g., via nanoconfinment or mixed solvents), will lead to effective Ln3+ separation.

Targeting Phosphotyrosine in Native Proteins with Conditional, Bispecific Antibody Traps.

Engineering sequence-specific antibodies (Abs) against phosphotyrosine (pY) motifs embedded in folded polypeptides remains highly challenging because of the stringent requirement for simultaneous recognition of the pY motif and the surrounding folded protein epitope. Here, we present a method named phosphotyrosine Targeting by Recombinant Ab Pair, or pY-TRAP, for in vitro engineering of binders for native pY proteins. Specifically, we create the pY protein by unnatural amino acid misincorporation, mutagenize a universal pY-binding Ab to create a first binder B1 for the pY motif on the pY protein, and then select against the B1-pY protein complex for a second binder B2 that recognizes the composite epitope of B1 and the pY-containing protein complex. We applied pY-TRAP to create highly specific binders to folded Ub-pY59, a rarely studied Ub phosphoform exclusively observed in cancerous tissues, and ZAP70-pY248, a kinase phosphoform regulated in feedback signaling pathways in T cells. The pY-TRAPs do not have detectable binding to wild-type proteins or to other pY peptides or proteins tested. This pY-TRAP approach serves as a generalizable method for engineering sequence-specific Ab binders to native pY proteins.

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative.

Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. Results Evaluated were 728 participants including 324 patients (mean age, 64 years ± 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years ± 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P < .001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P < .001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P = .009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P = .73). Conclusion The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Richardson in this issue.

DFT modelling of explicit solid-solid interfaces in batteries: methods and challenges.

Density Functional Theory (DFT) calculations of electrode material properties in high energy density storage devices like lithium batteries have been standard practice for decades. In contrast, DFT modelling of explicit interfaces in batteries arguably lacks universally adopted methodology and needs further conceptual development. In this paper, we focus on solid-solid interfaces, which are ubiquitous not just in all-solid state batteries; liquid-electrolyte-based batteries often rely on thin, solid passivating films on electrode surfaces to function. We use metal anode calculations to illustrate that explicit interface models are critical for elucidating contact potentials, electric fields at interfaces, and kinetic stability with respect to parasitic reactions. The examples emphasize three key challenges: (1) the "dirty" nature of most battery electrode surfaces; (2) voltage calibration and control; and (3) the fact that interfacial structures are governed by kinetics, not thermodynamics. To meet these challenges, developing new computational techniques and importing insights from other electrochemical disciplines will be beneficial.

Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC).

OBJECTIVE: To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network. METHODS: Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images. RESULTS: Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUVmean was - 0.10 ± 2.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, - 0.83 to 1.18). SUVmax had mean RE (%) of - 3.87 ± 2.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of - 3.99 ± 2.11 (range, - 8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 ± 2.99. CONCLUSIONS: Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners. KEY POINTS: • We demonstrate direct emission-based attenuation correction of PET images without using anatomical information. • We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images. • Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.

Anodic decomposition of surface films on high voltage spinel surfaces-Density function theory and experimental study.

Oxidative decomposition of organic-solvent-based liquid electrolytes at cathode material interfaces has been identified as the main reason for rapid capacity fade in high-voltage lithium ion batteries. The evolution of "cathode electrolyte interphase" (CEI) films, partly or completely consisting of electrolyte decomposition products, has also recently been demonstrated to correlate with battery cycling behavior at high potentials. Using density functional theory calculations, the hybrid PBE0 functional, and the (001) surfaces of spinel oxides as models, we examine these two interrelated processes. Consistent with previous calculations, ethylene carbonate (EC) solvent molecules are predicted to be readily oxidized on the LixMn2O4 (001) surface at modest operational voltages, forming adsorbed organic fragments. Further oxidative decomposition of such CEI fragments to release CO2 gas is however predicted to require higher voltages consistent with LixNi0.5Mn1.5O4 (LNMO) at smaller x values. We argue that multistep reactions, involving first formation of CEI films and then further oxidization of CEI at higher potentials, are most relevant to capacity fade. Mechanisms associated with dissolution or oxidation of native Li2CO3 films, which are removed before the electrolyte is in contact with oxide surfaces, are also explored.